How to find a data analytics job

A data job search runs through two different kinds of tool, and conflating them wastes time. One kind finds you open roles: general or niche job boards. The other prepares you to pass the interview once you have an application in: practice-question banks and company-specific guides. Neither substitutes for the other — a strong DataLemur streak does not get you an interview, and a stack of interviews does not help if you never applied. This guide is for anyone job-hunting as a data analyst, data scientist, data engineer or analytics engineer. If you are hiring rather than applying, the calculus is different — you care about time-to-hire and pipeline quality, not which practice site has the better question bank.

Niche board or general board

The first decision is where to look for openings, and it comes down to a trade-off between volume and relevance.

A niche board filters to your field before you ever open a listing. ai-jobs.net lists only AI, machine learning, data science and big data roles, filterable by seniority, salary range, stack and job type, with email and RSS alerts. Because it only accepts paid employer postings, there is less of the stale, copy-pasted-from-elsewhere listing you find on boards anyone can post to for free — a real, if imperfect, quality signal.

A general tech board with rich employer content casts wider but adds a different kind of signal: what a company is actually like to work at. Welcome to the Jungle builds job listings around photo, video and written profiles of how a company works, across tech, product and data roles together, and since absorbing the job-matching platform Otta in 2024 it carries over Otta's preference-based matching for candidates who migrated. It is free for job seekers either way.

Neither is exhaustive. Most searches that go well use a niche board to catch roles a general board's filters miss, and a general board (or your network) to gauge culture and compensation range before you invest time in an application.

What "free for candidates" actually means

Every tool in this category is free for job seekers to browse and apply — because the business model charges the employer, not the candidate, for a listing or a hiring pipeline. That matters for how you read a board: a board with only paid postings (ai-jobs.net) has a real economic reason for a company to keep listings current, because a stale post is still costing them money. A board that also sells recruiting software and matching tools to employers (Welcome to the Jungle's Hiring Suite) has an incentive to surface roles its paying customers post prominently. Neither is a problem for you as a candidate, but it explains why the same search on two boards does not return the same list.

Practising for the interview is a separate job

Once you have an interview, question-bank sites solve a different problem: most data interviews ask you to write SQL, reason about statistics or machine learning, or walk through a product-sense case study live, under time pressure, in front of someone watching. That is a skill you build by doing it repeatedly under similar conditions, not by reading about it.

DataLemur and Interview Query both run SQL and data-science questions in a browser code editor and attribute questions to the companies reportedly asking them, and both assume you already know SQL and basic statistics — they drill interview performance, they do not teach the underlying skill from zero. The difference between them is breadth and price. DataLemur is priced for volume (monthly, annual or a one-time lifetime purchase, all comparatively inexpensive) and keeps its scope to SQL and data-science fundamentals. Interview Query is a subscription-only product with a much larger catalogue of company-specific interview guides — over 1,600 at last count — describing what a particular employer's loop actually looks like, plus course content beyond question drilling. If you are applying broadly and want cheap repetition, start with DataLemur; if you are down to a shortlist of specific employers and want to know their process, Interview Query's guides are the more direct fit. A third option in the same space, StrataScratch, adds product-sense and case-study prompts tagged by company and is worth comparing if neither of the other two fits your budget — see choosing an analytics course for where it sits next to the learning platforms.

Build the underlying skill before you drill it

Question banks are a poor place to learn SQL or statistics for the first time — they assume competence and measure it under pressure, which is a frustrating way to discover a gap. If your SQL is shaky, close that gap with a course or free tutorial first, then move to interview drilling once the fundamentals are solid. The analytics course guide covers that stage.

A shortlist by situation

• You want the widest net of genuinely data/AI-labelled openings: start with ai-jobs.net and add a general board for anything it misses.
• You want to evaluate culture and comp before applying, not just after an offer: Welcome to the Jungle's employer profiles are built for this.
• You are applying broadly and want cheap, repeated SQL and stats practice: DataLemur.
• You have specific target employers and want their interview format in advance: Interview Query's company guides.

Questions to ask before paying for a practice site

• Does the question bank cover the languages and topics your target roles actually use (SQL only, or also Python, product sense, ML)?
• How current are the company-specific guides — interview loops change, and a two-year-old guide can mislead as easily as help?
• Is there a free tier large enough to judge fit before you commit to a subscription or lifetime purchase?
• Does a lifetime plan make sense for a search you expect to finish in a few months, or is a monthly plan the better bet?

Common mistakes

• Treating a practice-question streak as proof of readiness. These sites train pattern recognition on the exact interview format; they do not replace mock interviews with another person, where you have to think out loud under real pressure.
• Applying only through one board. Employer budgets and posting habits differ; the same role can appear on one board and not another.
• Skipping the fundamentals stage. If a practice question stumps you because you do not know the SQL concept, not because you are nervous, go build the skill before you keep drilling.
• Ignoring the free tier's limits. Most of these tools cap the free tier hard enough that a real evaluation needs at least a short paid trial; budget a small amount to test properly rather than guessing from marketing pages.

For a head-to-head look at two specific choices, see ai-jobs.net vs Welcome to the Jungle and DataLemur vs Interview Query. The full list of tools in this category is at every tool in this category.