I have been working as a data analyst for 7 years, 2 years of internships and 5 years full-time. I have a degree in Business Intelligence and Analytics it helped to land my first job, but I have a constant feeling that if I stop learning, I could fall behind. (Of course, it’s the case for many other professions, but in tech, you feel it more.)

In that time, I have watched the role change significantly. When I started, SQL, Python, and Power BI were enough. Last time I changed companies, I noticed that requirements and responsibilities are changing , more position for hybrid profiles part analyst, part engineer. Competition is getting tougher and now with AI, budgets are being cut and the bar for entry is rising.

My honest feeling: it will get harder for beginners every year from here. Not impossible, but harder. and not only for beginners 

So if you are starting now or switching careers into data, here is what I wish someone had told me.

Start top-down, not bottom-up.

Most people start with tools. They open a SQL course, memorize syntax, and then wonder what to do with it.

That is backwards.

Before you learn a single tool, research requirements and responsibilities of Data Analyst positions in the companies you want to work at. List the skills they ask for, list the responsibilities, and try to understand what the job is actually for.

Ask yourself:

• Which industries actually interest me?

• What does a day-to-day look like in the

companies

• I am targeting?

• What problem does this analyst solve for the business?

• What is the goal of the role?

This will give you a global view and the idea about the job.

This is more for private sector companies, but in the public sector, some things will still be applicable.

Learn how businesses make money before you learn how to query a database.

This is the thing nobody teaches—and it is the thing that separates analysts who get promoted from analysts who stay stuck executing requests.

Every business runs on the same fundamental loop:

Attract customers → Convert them → Deliver value → Retain them → Grow revenue

The department you work in determines which part of that loop you spend most of your time on. Your analysis will always serve one or many of those stages.

Once you understand that, you stop thinking about data as numbers and start thinking about it as evidence for decisions.

Four questions to ask before any piece of analysis:

1. What is the business actually trying to achieve, the real goal, not just the metric?

2. Who will use this analysis? A CEO and a product manager need completely different things from the same data.

3. What decision does this analysis need to support? Analysis without a decision attached is just exploration.

4. What action will they take after seeing the numbers? If the answer cannot change anything, the question is not worth asking.

Build this thinking into every project from day one and you will be ahead of most junior analysts within six months.

Learn tools to answer questions , not to memorize syntax.

Once you understand the business context and the types of questions you will be asked, tools become obvious. You learn SQL because you need to query customer data. You learn Python because you need to clean a dataset or automate a report. You learn Power BI because a stakeholder needs to interact with a trend, not just read a table.

Learning a tool without a question to answer is why most people plateau. The syntax does not stick because there is nothing to attach it to.

Use AI and Kaggle to find datasets that match your target industry. Build projects around real business questions. "What is driving churn in a subscription business?" is a better starting point than "let me practice SQL joins."

Talk to people already doing the job.

There is more useful information in one honest conversation with a working analyst than in ten hours of tutorials. Threads, LinkedIn, Reddit,you will find those willing to help if you ask a specific question rather than a generic one. Prepare it in advance, though.

If this 'top-down' approach makes sense to you but you aren't sure how to map it out, I’ve put it together in a free guide that you can find it in my bio link."

If you have any questions, feel free to ask in the comments or send me a DM.

how is bros name Power BI, And he's a data analyst!

Put together this one-page SQL revision sheet for analyst interviews.

I tried to cover the things that tend to matter beyond just remembering syntax: joins, aggregation grain, NULL handling, CTEs, window functions, common interview patterns, and mistakes that can quietly give you the wrong result.

For people who've actually done data analyst interviews, what SQL topic or question pattern would you add?

I don't usually post here, mostly reading, but this kind of story helped me stay motivated when I was starting so I want to give back a little.

Quick background: I did a master's in economics. Near the end I realized the only parts I really enjoyed were the data parts. The rest, not so much. So I decided to pivot to data analysis, with zero technical background.

Here is honestly how it went, including the parts that didn’t work really well:

Phase 1: YouTube. Actually a good start. There are some really well made videos out there and they helped me understand what data analysis even is, what the tools do, what the job looks like. I watched a lot of tutorials. But at some point I felt I was just watching, not doing.

Phase 2: Coursera. I wanted something more serious so I tried it. It just didn't fit me. It felt like more courses, more videos, more certificates, and that was not what I was looking for. Not saying it's bad, it just wasn't for me.

Phase 3: Building. What changed everything is when I stopped trying to "finish learning" and started building a portfolio instead. I used d8a academy for this, because the whole idea is that you learn by doing the projects, so your portfolio builds itself while you learn. That was perfect for me. I completed the whole data analyst path, built my first real projects, and somewhere in the middle of that I realized I actually love this. Like, for real, not just as a career move.

The job search part. This is maybe the most useful thing I can share. I was looking at offers on LinkedIn, plus the job offers on the platform since I was already there anyway. For every offer that looked like a possible fit, I added the recruiter on LinkedIn. Even when I didn't apply. Just connected.

And one day, one of these recruiters contacted ME, for a different position than the one I had seen. Honestly I don't even remember anymore if it was a recruiter from an offer I saw on the platform or somewhere else, I added quite a few. We talked, I walked her through my projects, did the interviews, and I signed last week. First contract as a data analyst. Still a bit unreal to be honest.

If I could tell something to myself one year ago:
- You don't need to feel "ready" before building something. You will never feel ready. Build anyway.
- Your projects are your best interview answers. I talked about them way more than about my degree.
- Add recruiters on LinkedIn from offers that match you, even if you don't apply. It costs nothing and it's how I got my job.

Not saying my way is the only way, it's just what worked for me. If you're stuck in tutorial mode right now, pick a small project and start it this week.

Good luck everyone, you got this :)

I used to think being good at SQL basically meant knowing JOIN, GROUP BY, CTEs, subqueries, window functions, and maybe a bit of query optimization. Then I started looking at SQL from a data warehouse perspective, and realized there are a bunch of things that aren't necessarily difficult syntactically, but become extremely crucial when you're dealing with millions or billions of rows and data arriving continuously.

Here are a few things I wish I had paid more attention to earlier.

1. ROW_NUMBER() can solve countless warehouse challenges.

Most people learn ROW_NUMBER() as a ranking function. For example like:

SELECT
    employee_name,
    salary,
    ROW_NUMBER() OVER (
        ORDER BY salary DESC
    ) AS salary_rank
FROM employees;

That's fine, but one of its most useful warehouse applications is deduplication.

Imagine your source system sends this:

customer_id | email              | updated_at
------------+--------------------+-------------------
101         | [email protected]      | 2026-09-15 09:00
101         | [email protected]      | 2026-09-15 11:00
101         | [email protected]      | 2026-09-15 11:05
102         | [email protected]     | 2026-09-15 10:00

If customer 101 appears three times, which record should enter your warehouse?

You can do:

WITH ranked AS (
  SELECT *,
   ROW_NUMBER() OVER ( 
    PARTITION BY customer_id
    ORDER BY updated_at DESC 
    ) AS rn 
  FROM staging_customers 
)

SELECT * 
FROM ranked 
WHERE rn = 1;

Now you get:

101 | [email protected]  | 2026-09-15 11:05
102 | [email protected] | 2026-09-15 10:00

This pattern becomes incredibly useful when dealing with:

• duplicate API records

• repeated file loads

• CDC data

• retry events

• multiple updates to the same entity

• late-arriving records

So ROW_NUMBER() isn't merely about ranking but also it can become a data-quality and ingestion tool.

2. MERGE becomes important for incremental loading

When you're learning SQL, you might be perfectly happy doing:

DELETE FROM customers;

INSERT INTO customers
SELECT *
FROM staging_customers;

But imagine the warehouse contains 20 million customers and only 5,000 changed today. Reloading all 20 million every day isn't exactly elegant. Instead, you can use an incremental approach.

For example:

MERGE INTO dim_customer AS target
USING staging_customer AS source
ON target.customer_id = source.customer_id

WHEN MATCHED THEN
    UPDATE SET
        customer_name = source.customer_name,
        email = source.email

WHEN NOT MATCHED THEN
    INSERT (
        customer_id,
        customer_name,
        email
    )
    VALUES (
        source.customer_id,
        source.customer_name,
        source.email
    );

Conceptually:

Source record
      |
      v
Does customer already exist?
      |
   +--+--+
   |     |
  YES    NO
   |     |
 UPDATE INSERT

For example:

Existing warehouse:

101 | John | [email protected]
102 | Mary | [email protected]

Today's source:

101 | John | [email protected]
103 | Peter | [email protected]

After the merge:

101 | John  | [email protected]
102 | Mary  | [email protected]
103 | Peter | [email protected]

This is the kind of thing that becomes helpful when you're designing incremental pipelines because you don't want to reload everything every time a source changes.

3. LAG() can become a change-detection tool

Another one that initially looks like a reporting function but becomes very useful in warehouse pipelines is LAG().

Imagine your source sends daily customer snapshots:

customer_id | email          | snapshot_date
------------+----------------+--------------
101         | [email protected] | 2026-09-14
101         | [email protected]  | 2026-09-15
101         | [email protected]  | 2026-09-16

You want to know when the email actually changed.

Try:

SELECT
    customer_id,
    email,
    snapshot_date,

    LAG(email) OVER (
        PARTITION BY customer_id
        ORDER BY snapshot_date
    ) AS previous_email

FROM customer_snapshots;

You get something like:

101 | [email protected] | 2026-09-14 | NULL
101 | [email protected] | 2026-09-15 | [email protected]
101 | [email protected] | 2026-09-16 | [email protected]

Now you can identify changes:

CASE
    WHEN previous_email IS NULL
         OR email <> previous_email
    THEN 1
    ELSE 0
END AS changed

Now the warehouse can distinguish:

2026-09-14 → initial record
2026-09-15 → CHANGED
2026-09-16 → no change

This becomes especially interesting when implementing Slowly Changing Dimensions, audit history, or incremental transformations.

4. Use surrogate keys instead of blindly using source IDs

Suppose your CRM gives a customer:

customer_id = 4587

It's tempting to simply use that as your warehouse primary key.

But warehouses often create their own surrogate key:

customer_key | customer_id | customer_name
-------------+-------------+--------------
1            | 4587        | John
2            | 7321        | Mary
3            | 9845        | Peter

Here:

customer_id

is the business/source key.

While:

customer_key

is the warehouse surrogate key.

Why does this matter?

Imagine a customer exists in your CRM:

CRM customer_id = 4587

and later the company migrates to another CRM.

The new system might assign:

customer_id = 78231

If your warehouse architecture is tightly coupled to the source ID, that can create problems.

Surrogate keys allow the warehouse to maintain its own identity. They also become particularly useful when tracking historical versions of dimensions.

For example:

customer_key | customer_id | city     | start_date | end_date
-------------+-------------+----------+------------+----------
101          | 4587        | Nairobi  | 2026-01-01 | 2026-08-10
205          | 4587        | Mombasa  | 2026-08-11 | NULL

Same business customer:

customer_id = 4587

but two warehouse records because the customer's historical state changed. That's a very different concept from simply storing the latest customer record. In essence, surrogate keys give your warehouse its own stable identity.

5. Use conditional aggregation to build warehouse metrics in one pass

Suppose you have:

orders

with:

order_id | status
---------+----------
1        | completed
2        | completed
3        | cancelled
4        | pending
5        | completed

You could run three separate queries:

SELECT COUNT(*)
FROM orders
WHERE status = 'completed';

Then another for cancelled.

Then another for pending.

Or you can build the metrics together:

SELECT
    COUNT(*) AS total_orders,

    SUM(
        CASE
            WHEN status = 'completed'
            THEN 1
            ELSE 0
        END
    ) AS completed_orders,

    SUM(
        CASE
            WHEN status = 'cancelled'
            THEN 1
            ELSE 0
        END
    ) AS cancelled_orders,

    SUM(
        CASE
            WHEN status = 'pending'
            THEN 1
            ELSE 0
        END
    ) AS pending_orders

FROM orders;

Result:

total | completed | cancelled | pending
------+-----------+-----------+--------
5     | 3         | 1         | 1

Now imagine doing this by:

country
product
month
customer segment
sales channel

You can build extremely useful analytical tables from relatively simple SQL.

Some databases also support:

COUNT(*) FILTER (
    WHERE status = 'completed'
)

which can make this even cleaner. This technique is extremely useful for building data marts and BI-ready tables.

This is probably the biggest lesson I've taken from all of this. For people who have actually built data warehouses, what's one SQL technique, design principle, or 'small detail' that seemed unimportant when you were learning SQL but became absolutely critical once you started working with real warehouse data?

I'm trying to switch careers from construction to data analytics & after a month, I have successfully completed my first project using both SQL and Excel. Attached is a screenshot of a dashboard I built on a company's pizza sales. Any advice and tips on how to improve are greatly appreciated.

I see many posts here with folks asking how to land their first job, will AI take away jobs, what do they have to learn to land jobs?

The truth — “data analysis” or “data analyst” is not a job! It’s a skill! You’re asking the wrong question.

For perspective, think of this: Imagine someone asking, “if I keep working on my shooting skills, can I get a contract in the NBA?”. The answer is a big NO! Because there’s more than just shooting to basketball.

Solution(s): find an industry or sector you want to work in that uses data analysis skills. Then research the heck out of specific roles where the data skills will come in handy. Finally, learn that industry / business inside out and apply the data analysis skills there. That’s what gets you a job. That what gets you high pay. Crunch numbers, building dashboards means nothing if you don’t apply it to an industry and bring VALUABLE INSIGHTS to the table.

tl;dr — you’re not going to get a job with SQL, python, tableau, and power BI. you’ll get a job when you APPLY those skills to an industry!

I'm applying to data analyst/science internships since I have no formal work experience in the field.

Please, please, please do not respond by telling me that my work experiences are not data analyst experiences. I obviously know that. I'm asking for more constructive feedback—things I can change now as I am looking for roles.

Don't be gentle; I've been unemployed for so long that I have gone numb, and so my feelings cannot be hurt.

EDIT: I forgot to mention that I am looking for internships that are within healthcare or HR/people analytics. I am not interested in working in tech - been there, done that, and I'm not interested.

EDIT #2: Wow, thank you for all the constructive feedback! I haven't had a chance to read everyone's post, and I definitely haven't had a chance to respond to everyone I want to respond to, but I plan on it. Meanwhile, I wanted to express my gratitude to each of you for taking the time to glance at my resume and respond.

EDIT #3: Some of you have asked me what I have been doing during my 22-month employment gap. Instead of constantly responding to everyone, I am going to include a timeline of my activities since July 2024 (the last month I worked full-time):

• July 2024: I was laid off because the company dissolved

• August 2024 to May 2025: I took courses at a community college to complete prerequisites for the grad programs I was applying for. I also took this time to attend networking events locally.

• May 2025 to August 2025: I did some freelance work and went to conferences and networking events. I applied for a few roles during this time and had two interviews for DA roles. For both roles, I went through all rounds of the interview process without receiving an offer.

• August 2025 to March 2026: Started my master's program. During this time, I explored some of the resources available to me at school (i.e., career counseling, research opportunities). I did not apply to many jobs during this period because I wanted to focus on my courses and adapt to the graduate school workload. I also took this time to explore the domains within data analytics so I could tailor my academic coursework and projects toward those industries.

• Present: Now I'm looking for internships for the summer or fall, primarily to fill in the employment gap in my work history. My graduation date is May 2027, and I would like to have completed an internship or two before then.

I wanted to thank each and everyone from this subreddit alongside the resumes community for providing such valuable and helpful feedback on brushing my resume and interviewing approach.

The secret sauce is being clear and flexible to provide valuable impact on business and people. In addition, have strong persistence to take feedback seriously and regularly attend networking events in person and online.

I got multiple job offers and ongoing interviews every week with just one interview, nothing too technical and crazy since they were amazed that I used technology to create positive and meaningful impact. Vibes are most important factor in a job interview and job too.

The reasons why I included education at the bottom is because I already have years of experience and that experience holds the most weight.

Here’s a free Google Docs Template with helpful pointers:

https://docs.google.com/document/d/1-EE11tAh9icmg4gFtiPkTuf5GCWh9AA5xdWTaLom7OE/

I think my story can help some of the data folks here so I'll try to summarize it here with no bullshit haha.

few years ago I was a business school student with zero data skills. Now I freelance at $750/day for luxury brands and big corporations. Here's what actually worked for me.

The lucky break

Did a marketing internship at an insurance company. Got introduced to Google Analytics and loved it. They asked me to cover a web analyst role part-time during my final year. I said yes and taught myself dashboards and reporting on the job.

The pivot

Did a Master's that combined business school + engineering school. Best decision ever, I can talk to both business people and tech teams.

First real job

Got hired at a major luxury fashion house as a junior data analyst during my Master's. What got me in? I showed them the actual dashboards and reporting systems I'd built at the insurance company. Not theory -> real stuff.

Going freelance

After my Master's, went freelance. Started at $400/day, now at $750/day. Worked for luxury brands, a global ad agency, a music company, an industrial distributor.

The only thing that matters

Every single client hired me because I showed them I'd already done what they needed. Not "I can do this", "I've done this, here's the proof."

• Luxury luggage brand hired me because I talk to then about the e-commerce dashboards from my previous role

• Industrial company hired me because I showed BigQuery optimization examples

• Ad agency hired me because I explained with details ad-hoc analyses I'd done for executives

Advice

1. Build real projects. Doesn't matter if they're small.

2. Before any interview, figure out what they need and show them you've already done it.

3. Master SQL, dashboards, and how to explain data to non-technical people. That's 90% of the job.

The whole game is proof. Nobody cares what you can do. Show what you've done.

Ressources you could use

• Find cool data science project on https://www.kaggle.com

• Roadmap and data analysis project + community on https://d8a.academy

• Use https://claude.ai to help you learn SQL syntax and python. You shouldn't let it code everything but accelerate your learning.

Hope this help or motivate some of you, Good luck !

Hey Reddit,

I graduated in May 2025 with a Master’s in Information Systems and have 3 years of experience as a Data Analyst / Integration Engineer — mostly in healthcare and insurance (EDI 834/835/270/271, Python, SQL, Power BI, Snowflake, Salesforce CRM).

I’ve applied to over 1000 roles in the past few months — entry-level, mid-level, contract, W2 — but very few interviews and no offers. Even referrals haven’t led to anything.

I’ve tried:

Tailored resumes + custom cover letters

Cold outreach on LinkedIn

Contract roles via recruiters

I’m starting to burn out and feel completely stuck. What has worked for anyone recently in data/analytics? Is the market just too saturated right now? Should I pivot short-term?

Any advice or shared experience would be hugely appreciated 🙏

used to think knocking out LeetCode puzzles and getting a badge meant I was ready for analyst interviews. I learned the hard way that teams don't care about brain teasers.

In my job now, it’s all boring, messy SQL where you just have to prove your numbers are right.

I tanked my early interviews because I panicked when asked to predict a row explosion during a join, or how to handle duplicates with ROW_NUMBER without lying with the data. I had to teach myself to use window functions for everyday metrics, build readable CTEs, and stop scanning massive tables for no reason.

Things changed when I started writing query summaries in plain English. Added that to my resume with resumeworded. This helped me see exactly where my phrasing fell flat compared to what employers wanted. It showed me how poorly my technical jargon translated and guided me to rewrite those summaries into impact-driven bullets that finally got me callbacks.

It’s wild how much time I wasted on coding puzzles when the actual job is just trying not to mess up basic null handling.

Hi everyone — I’ve been applying to data analyst roles but not getting many callbacks.
I’d really appreciate honest feedback on my resume, especially from anyone with hiring experience or working as a data analyst.
What might be holding me back?

I posted a DA position for $70k and just read 225 resumes that got past HR. 200 had Master’s in DA/DS/Comp Sci, etc and all of those had 3-5 years experience with what seemed like the same 15 companies (lots of health data, finance, Indian consultancy companies for FAANGs).

None of them had the content experience I’m looking for but that’s hard.

I can’t imagine trying to get in, now, with just a BS in anything and I can’t find a reasonable way to differentiate the otherwise identical resumes.

Maybe just a rant, but keep this stuff in mind if you’re just getting started with a DA Bootcamp and/or a Bachelor’s

I get the impression many of you might just be looking for “Data Analyst” jobs in your search. Don’t limit yourself to just that.

BI Analyst, Business Analyst, Operations roles, Marketing roles all may have some heavy data and reporting components to it where your SQL, python, and data viz skills will be very helpful!

For context, I quit my consulting job with nothing lined up about 5 years ago. The only skills I had from that role were SQL, Tableau, and some company-specific applications. I met a guy out in New York who was the CEO of a fast-growing startup and asked if he needed a data guy. I flew in for 5 in-person interviews and got the job. I used my SQL and Tableau skills, added Python and Excel, and was promoted to Lead Data Analyst after 1 year and more recently to Head of Data after making some large contributions to the company’s culture and top line.

We were acquired by our top investor group and now I mostly do data analyst mentoring on the side. I’ve seen countless mistakes that people make both in the application process and after being hired. I’d love to answer some questions for you all!

Lately, I've been watching a lot of YouTube videos from data analyst creators, and one thing I've noticed is that even their views seem to have dropped significantly compared to a year or two ago.

Earlier, Data Analytics was everywhere. Everyone was talking about becoming a Data Analyst, learning Excel, SQL, Power BI, Python, building projects, and landing a high-paying job.

But now I'm starting to wonder whether the market is becoming oversaturated or whether the demand for data analysts is actually declining.

I'm also seeing so many people learning the same skills, building similar portfolio projects, and applying for the same entry-level positions. On top of that, AI tools are becoming increasingly capable of handling basic data analysis tasks.

Honestly, it's making me question whether pursuing Data Analytics in 2026 is still a smart career decision, especially for beginners who are trying to break into the industry.

I genuinely want to know:

1. Is Data Analytics actually declining as a career, or is the YouTube hype just fading?

2. Are companies still hiring freshers and entry-level data analysts?

3. Has AI reduced the demand for junior data analysts?

4. If someone starts learning Data Analytics today, is there still a realistic chance of building a good career in this field?

I'd really appreciate honest opinions, especially from people currently working as Data Analysts or those who recently entered the industry.

I don't want motivational answers. I want to understand the actual market reality.

Hi, I'm Marius, senior data analyst with almost 10 years of experience.

I created d8a academy, a learning/job platform. Last month a student of ours shared her story on how she found her first job as Data analyst junior here and our Reddit traffic exploded. Lots of you join the community, the messages were really kind, and we figured: if Reddit is sending us this much love, we should answer with as much value as we can. So here is the list of every free ressources from our website (I made a landing page with the link of all free ressources here.

• The intro course of each path (Data Analyst, BA, Data Engineer, Data Scientist): free, no account needed. There's a SQL playground that runs in your browser.

• The portfolio builder: drop your LinkedIn PDF, get a live page you can send to recruiters. Parsed in your browser, never uploaded.

• A cheatsheet every day (SQL, Python, Excel, stats...). Download them, print them, tape them next to your desk or on the toilet door.

• The weekly challenge: a few questions every week, a leaderboard, a bit of friendly competition to keep you moving.

• The Discord: about 900 people, career changers helping each other and reviewing each other's projects. Being a paying member is not required to belong there.

• The job feed: real data roles from our LinkedIn network, reviewed by hand. Every offer opens free 3 days after it lands.

Yes there is a subscription that unlocks the full project paths, that's how we pay the bills. A minority of members pay, and that keeps everything above free for everyone else. Without paying you can still be part of the community and ask all your question on discord, find people to study with, etc.

Thanks for the support these past weeks. Questions in the comments, I'll answer everything.

EDIT: The post I'm mentioning is this one

Excited to share my latest Power BI project — a FIFA World Cup 2026 Tournament Analytics Dashboard.

I designed this dashboard to analyze the tournament from multiple perspectives:

📌 Page 1 — Matches Overview

A complete view of tournament-level match data, including:

• 104 Matches

• 328 Total Goals

• 3.15 Average Goals per Match

• Attendance Analysis

• Goals by Tournament Round

• Home vs Away Performance

• Possession, Saves, Fouls, Crosses & Offsides

• Interactive filters for deeper exploration

🏆 Page 2 — Top 3 Performers

A focused comparison of the top-performing teams across key metrics such as:

• Goals Scored

• Defensive Performance

• Possession

• Shots on Target

• Fouls

• Cards

🥇 Page 3 — Executive Summary

A high-level summary of the tournament, highlighting:

• Champion

• Runner-up

• Third Place

• Best Attack

• Best Defense

• Fair Play Team

• Highest Attendance Match

• Biggest Win

• Highest-Scoring Match

The goal was not just to visualize football statistics, but to structure the dashboard as a complete analytical story — moving from full tournament data → top performers → key tournament outcomes.

🛠️ Tools & Skills:

Power BI | DAX | Power Query | Data Modeling | Data Visualization | Data Analysis

This project helped me further improve my skills in transforming raw data into an interactive dashboard that communicates insights clearly and effectively.

I’d appreciate your feedback:

What would you improve or add to this dashboard?

Just saw a preview of Claude’s dashboard insights capabilities. It’s not looking good for apps like Power Bi and Tableau.

With Claude’s new release, do you think data analytics is still worth pursuing a career in?

I‘m sure it won’t completely replace Data Analysts but, it’ll sure make the effort to.

Hai Guyz,

I have become the Official data Visualizer for the white house because I am the best in the country and not because my dad is a golfing buddy of the POTUS. I am willing to take any and all questions. Trump 2028!!??

After 219 applications and interviews with 17 companies over 6 months, I finally received a verbal and written offer from a Fortune 500 insurance company for a Marketing Analytics Consultant role.

I still can't believe it's real. The process started with a cold LinkedIn application on 03/24/2026 and ended with an offer today 05/28/2026 — just over 2 months from apply to offer for this particular role.

The salary is 30% higher than my previous role, with benefits including 39 total paid days off, 7 remote weeks, onsite fitness center, cafeteria, beautiful waterfront office, and wellness/commute/education reimbursements. I genuinely couldn't ask for a better offer at this stage of my career.

To everyone still searching — don't lose hope. Keep applying, keep improving, and the right opportunity will come. It took me 219 applications to get here but it was absolutely worth it. 🙏

I just started a Junior Data/BI Analyst role after finishing my master’s in Economics, and I’m having an “oh shit, this is real” moment. I’d love some perspective from experienced analysts.

My previous experience was more policy/data analysis than a traditional data analyst role. In one position, I assisted with analysis for major policy/program changes using SQL and Excel. I could write/modify queries, but I often had existing queries to build from and senior people supporting the larger projects. It was an internship.

In another role, much of my work was recurring reporting: we’d receive new datasets/CSVs, I’d update existing Excel dashboards (not power bi), and distribute the reporting. I also handled ad hoc requests here and there. It was also an internship of 4 months

I’ve also used SQL, Python, Power BI, etc. through graduate school briefly, but if I’m being completely transparent, I relied pretty heavily on AI for some school assignments. I can usually figure things out, but I’m not someone who can necessarily sit down and immediately code a solution from memory.

Now I’ve joined a large organization that provides financing/insurance-type products to businesses (loans, guarantees, etc.). I have basically no finance background beyond economics, so I’m simultaneously trying to understand the products, business definitions, organizational structure AND the technical environment.

The job involves maintaining dashboards/scorecards errors, SQL and Python for extracting/transforming/analyzing data, exploratory/diagnostic analysis, data quality/integrity, documenting metric/business logic, automation and eventually more advanced analytics/AI. The team is also moving toward a more centralized data platform.

What’s freaking me out is that several people have emphasized that the technical component was a major reason they hired me and that the team really needs help improving documentation, workflows, data definitions/integrity and consolidating the data environment.

The weird thing is that their technical interview wasn’t insanely difficult. The live SQL included things like date conversion and ROW_NUMBER() OVER (PARTITION BY...). I made some syntax mistakes and still got hired, so they did actually see my technical level.

Now I’m seeing relational databases, primary/foreign keys, joins, etc., and wondering: what am I actually going to be expected to do? Am I likely to be writing complicated programs/database infrastructure from scratch, or is BI work in a financial organization more SQL queries, transformations, validation, troubleshooting, pandas, existing scripts/pipelines, reporting, etc.?

I’m also realizing that maybe part of why I don’t feel like I can “think like an analyst” yet is because I barely understand the business. It’s hard to investigate why a number looks wrong when I don’t even know what the number is supposed to represent.

Lastly, I’ve always learned best privately. I like being able to struggle with something, Google/use AI, make mistakes and eventually figure it out. My new desk is extremely open with coworkers/manager nearby and people walking behind my screen. Weirdly, that’s making me even more anxious because I feel like people can see me not knowing what I’m doing.

For experienced analysts: how normal is this during your first real analyst job? What would you expect technically from a junior? How long did it take before you understood the business/data enough to solve ambiguous problems independently? And should I start grinding SQL/Python courses after work, or focus on learning whatever comes up in the actual job?

I want to become good at this. I’m just scared that I’ve gotten good at figuring things out with help rather than actually being a good analyst, and now I’m about to find out the difference.