How much of a data analyst's day is spent writing SQL?

Most of it. On a typical day, 3 to 5 hours go into writing, testing, and fixing SQL queries. That's the bulk of the work, not a side skill. The queries aren't glamorous—you're pulling a table, checking if the numbers match what someone expects, joining it to another table, and formatting it so a non-technical person can read it.

Do data analysts use Python every day?

Not at the entry level, and often not later either. SQL and a spreadsheet tool cover most entry-level and mid-level analyst work. Python shows up more in data engineering, machine learning, or advanced analytics roles, not in the daily grind of a standard analyst seat.

What meetings does a data analyst attend?

A typical week includes a team standup, one or two stakeholder meetings where you present or explain a number, and the occasional call where someone wants to understand a dashboard they can't read on their own. The skill that matters most in these meetings is explaining a number in one sentence to someone who doesn't care how you got it.

Is being a data analyst boring?

Some of it, yes. Most days include stretches of repetitive query writing and data cleanup that nobody would call exciting. The interesting part shows up when a chart finally answers a question someone's been asking for weeks, or when you catch an error before it reaches a director's inbox.

What's the difference between a junior and senior analyst's day?

A junior analyst spends more time on execution: writing the queries, building the dashboard, fixing the data someone else flagged. A senior analyst spends more time deciding what should get built in the first place and pushing back when a request doesn't make sense. Both still write SQL most days. The senior version just spends less time on syntax and more time on judgment.

What tools does a data analyst use daily?

In order of frequency: SQL editor, a BI tool like Tableau or Looker, spreadsheets like Excel or Google Sheets, Slack or email, and Python (occasionally). SQL editor is used most, while Python ranks lower than many expect, especially at the entry level.

Is Data Analytics Still a Good Career to Enter?

The honest version of this question is: is it still worth the effort to break in, given all the noise about AI replacing analysts and the tech layoffs that have made headlines for the past few years?

Yes, data analytics is still a good career to enter. The job market is not what it was in 2021, and it would be dishonest to pretend otherwise. But demand for analysts across healthcare, finance, retail, government, and operations is real, consistent, and not going away. The people who are struggling to break in right now are mostly the ones targeting tech companies exclusively or trying to stand out with credentials instead of demonstrated skills.

Here's the full picture — what's actually changed, what hasn't, and what it means for someone trying to break in today.

What the job market actually looks like right now
Is AI replacing data analysts?
What's harder than it used to be
What hasn't changed
Who data analytics is still a good fit for
Who should think twice
FAQ

What the job market actually looks like right now

The 2021 and 2022 data job market was unusually hot. Tech companies were hiring aggressively, salaries were inflated, and a lot of people got into data analytics during that window. Then came the 2023 and 2024 tech layoffs, and a lot of those same people were back on the market at the same time new candidates were trying to break in.

That collision made the entry-level market feel harder than it actually is — because in tech, it is harder. Entry-level product analyst roles at software companies have more competition than they did 3 years ago, and some companies that were hiring junior analysts have cut those roles in favor of seniors and AI tools.

But tech is not the whole market.

Healthcare, finance, insurance, retail, logistics, government, and manufacturing all hire data analysts, and most of those sectors have seen steady or growing demand over the same period. These are not glamorous industries from a LinkedIn perspective, but they're where the bulk of entry-level analyst hiring actually happens. A regional hospital system or a mid-size insurance company doesn't make the news when it hires 12 analysts, but those jobs are real and they're there.

The Bureau of Labor Statistics projects that employment of data professionals will grow faster than average over the next decade. That projection isn't driven by tech — it's driven by the broader adoption of data systems across every sector of the economy. Most companies that weren't using data seriously 10 years ago are now building the function, and they need people to staff it.

Is AI replacing data analysts?

This is the question everyone asks, and the honest answer is: AI is replacing some of what analysts do, but it's creating more work at the same time, and the net effect for entry-level analysts is less dramatic than the discourse suggests.

What AI has changed: simple, repetitive reporting tasks are being automated faster than before. If your entire job was pulling the same 3 reports every week and emailing them to stakeholders, that job is under real pressure. Tools like Copilot and similar integrations are making it faster for non-technical people to get basic answers from data without an analyst in the loop.

What AI hasn't changed: someone still has to define the right questions to ask. Someone still has to validate whether the output is correct. Someone still has to connect the data finding to a business decision and communicate it to people who don't live in spreadsheets. Those are judgment and communication tasks, and they sit above where current AI tools operate reliably.

I work in data engineering now, and I use AI tools every day. They speed up my work significantly. They don't replace the judgment calls about what the data actually means or whether a pipeline is producing trustworthy output. The same is true for analysts. The floor for what you need to know has shifted — basic Excel reporting is genuinely under pressure — but the core of the analyst role is more durable than the headlines suggest.

The practical implication for someone breaking in: you need to be someone who can think about the business question, not just execute a query someone else defined. That's always been true, but it matters more now. SQL and a BI tool are still the foundation. Adding the ability to interpret and communicate findings clearly is what keeps the role valuable as tools get smarter.

What's harder than it used to be

A few things that are genuinely harder today than they were 3 or 4 years ago:

Tech company entry-level roles. More experienced candidates are competing for the same junior roles after layoffs. If you're targeting FAANG or well-known SaaS companies as your entry point, the bar is higher and the timeline is longer than it was in 2021.
Standing out with just a certificate. A Google Data Analytics certificate or a Coursera course was more differentiating 4 years ago. Now the market is flooded with certificate holders. The credential isn't worthless, but it's table stakes, not a differentiator. Portfolio projects and demonstrated SQL skills matter more.
Getting traction with a generic resume. A resume that lists tools without showing what you actually did with them performs worse than it used to. Hiring managers have more applicants and less patience for vague descriptions.
Getting responses from cold applications. Application volume on job boards is up. Response rates are lower. Building even a minimal network — a recruiter conversation here, a LinkedIn connection there — matters more than it did when companies were posting and actively sourcing.

What hasn't changed

The things that have always worked still work:

SQL is still the entry ticket. Every analyst role across every sector requires it. The complexity varies, but there is no realistic path to an analyst job that skips SQL.
Portfolio projects still differentiate. A SQL analysis on a real dataset, a Power BI dashboard connected to public data, a clearly explained business question and answer — these still move candidates from the pile to the phone screen faster than any credential.
Domain knowledge still opens doors. If you have 5 years in healthcare and you're transitioning to healthcare analytics, you're a more credible candidate than someone who knows the tools but has never seen a patient record. That hasn't changed and won't.
Relationships still matter more than applications. Getting a referral or a recruiter conversation gets your resume read. Most analyst jobs still get filled through some version of a network connection, not a cold apply.
Most of the work still happens outside tech. Healthcare, finance, retail, and government are stable, broad, and actively hiring. These sectors are less visible in the discourse but represent the majority of actual analyst jobs.

Who data analytics is still a good fit for

Data analytics is a strong career move if any of these describe you:

You're a career changer with domain experience in healthcare, finance, retail, or operations who wants to add a technical skill layer to an existing background
You're willing to spend 3 to 6 months building real skills — SQL, Excel, a BI tool — before applying, instead of applying immediately with only a certificate
You're targeting industries outside tech or are open to getting your first role somewhere other than a software company
You want a career with clear skill progression, stable demand across industries, and an income floor that's meaningfully higher than most non-technical roles
You're comfortable with ambiguity and with presenting your work to people who will question it

I broke in without a relevant degree, without a bootcamp, and without connections in the industry. It took about a year. The path was slower than I wanted and more iterative than any course described. But the job was real and the skills were transferable. That path is still available — it just requires more intentionality than it did when the market was handing out offers.

Who should think twice

Data analytics is probably not the right move if:

You're expecting to complete a 3-month bootcamp or certificate and immediately land a $90,000 tech job. That path existed briefly in 2021. It doesn't today.
You hate working with numbers and spreadsheets and are hoping the tools will handle all of it. The tools help, but the work is still fundamentally quantitative.
You're only interested in product analytics at tech companies and have no interest in healthcare, finance, retail, or operations. The tech market is real but competitive, and refusing to consider other sectors significantly narrows your options.
You want a creative or people-first role. Analyst work can be collaborative, but the output is always a number, a chart, or a recommendation supported by data. If that sounds tedious rather than satisfying, it's worth knowing before you invest months in preparation.

If you've been following me on LinkedIn, you've seen me say this a few times: the people who break in are the ones who treat the job search like a job, not a vibe. They apply systematically, build real projects, and target the right companies for where they are — not where they wish they were. The market rewards that approach. It just takes longer to reward it than the YouTube thumbnails imply.

If you want a day-by-day structure for building the skills and running the job search, Analyst Hive is the program I built for exactly this. 90 days, one task at a time, covering SQL, Excel, Power BI, portfolio projects, resume, networking, and interview prep.

FAQ

Is data analytics oversaturated?

The entry-level tech analyst market has more competition than it did in 2021. The broader market — healthcare, finance, retail, government, operations — is not oversaturated. The perception of saturation is driven by people competing for a small slice of the market (tech, remote, well-known companies) while ignoring the much larger slice where hiring is consistent. Targeting the right sector makes a significant difference in how saturated the market feels.

How long does it realistically take to break into data analytics?

For someone starting with no technical background, 6 to 12 months of focused preparation before landing a first role is realistic in the current market. That includes building SQL skills, completing 2 or 3 portfolio projects, running an active job search, and doing some networking. The people who take longer are usually the ones who spend too long learning and not enough time applying, or who apply only to highly competitive roles.

Do you need a degree to become a data analyst?

No, and there are plenty of working analysts who don't have a relevant degree. What matters more is demonstrated skill — SQL you can show, projects you can explain, and the ability to pass a technical interview. A degree helps with certain employers (government, finance, some large corporations) but is not a hard requirement across the market. I got hired without one.

Will AI replace data analysts entirely?

Not in any near-term timeframe. AI is automating repetitive reporting work and making basic data retrieval faster for non-technical users, but the judgment layer — defining the right question, validating the output, connecting findings to business decisions, communicating clearly to stakeholders — still requires a person. The role is evolving, but it's not disappearing. Analysts who treat AI as a tool rather than a threat will be more productive and more valuable than those who ignore it.

Is remote work still common for data analyst roles?

It's less universal than it was in 2021 and 2022, but remote and hybrid analyst roles are still common, especially in tech, consulting, and health insurance. Traditional finance institutions and hospitals have pulled back toward in-person more than other sectors. If remote is important to you, it's worth filtering for it explicitly rather than assuming — and being open to hybrid as a starting point that can flex over time.

Where to go from here

If the answer is yes and you want a structured path to get there, Analyst Hive is the 90-day program that walks you through it day by day. SQL, Excel, Power BI, portfolio projects, resume, recruiter outreach, and interview prep — built in the order that actually gets you hired. Month 1 builds your assets. Month 2 sharpens them. Month 3 gets you into interviews and negotiations.