Remote Data Analyst Jobs: Titles to Search, SQL Tests and Portfolio

Looking for data analyst jobs, remote or not, is harder than it should be because the same work hides under several titles, and one title can mean quite different jobs. This guide covers which titles to search, how to tell them apart from the posting, what the SQL test usually looks like, and what a portfolio needs to show.

One kind of work, several titles

Analytics titles depend on where the team sits in the company. These are the families you'll see most:

A few notes on reading these:

• Product Analyst is often the closest thing to "data analyst at a tech company". If you enjoy experiments and product questions, search this title first.
• BI roles are more tool-heavy. When the posting names one BI tool, take it seriously; it's the first thing a screener checks.
• Analytics Engineer is closer to engineering: version control, code review, tests on data models. Plenty of analysts move here, and it's a sensible step if you like building models more than presenting findings. We compare the engineering side in Data Engineer vs Data Scientist vs ML Engineer.
• Business Analyst is the least consistent title. In some companies it's a data role; in others it's requirements and process work with no SQL at all.

You'll also meet Insights Analyst, Growth Analyst, Marketing Analyst and Analytics Specialist, plus seniority prefixes: Senior, Lead, Staff, Principal.

Read the posting, not just the title

The title gets you to the posting; the description tells you the job. Check four things:

1. The tools. SQL is a given. Python or R, dbt, a BI tool, or product analytics tools such as Amplitude or Mixpanel tell you what your days would look like.
2. Who you serve. A product manager, a finance lead, a marketing team or a head of data. That shapes the questions you'll get.
3. The verbs. "Build dashboards", "design experiments", "model data" and "partner with stakeholders" are four different jobs.
4. Experiments. A/B testing, statistical significance and causal inference mean a product analyst skill set, whatever the title says.

We go through this habit in more detail in how to read a job description.

What the SQL test usually looks like

Formats differ between companies, but a few shapes come up again and again:

• Live SQL in a shared editor. Joins, aggregations, window functions, and often a slightly messy question about dates.
• Take-home task. A dataset and a few business questions. You send back queries and a short write-up.
• Case discussion. No code at all: "sign-ups dropped last week, how would you investigate?"
• Metrics and product sense. Common for product analysts: define a success metric for a feature, or design an experiment and explain how you'd read the result.

How to prepare

• Practise the patterns that keep appearing: window functions (ROW_NUMBER, LAG, running totals), deduplication, retention cohorts, funnel conversion, date bucketing and NULL handling.
• Say your assumptions out loud. Before writing a query, state what you think a row represents and how you'll treat duplicates. Interviewers often care more about this than about syntax.
• Sanity-check the result. Compare a count against something you know, and say what you'd verify with more time.
• Time-box take-homes. Put the answer first and keep the write-up short. If the task looks much bigger than the time they suggest, it's fair to ask what they expect.

A portfolio that actually gets read

You don't need ten projects. Two or three solid ones are more convincing than a long list.

• Pick questions a business would care about. "Which sign-up cohort retains best, and why?" says more about you than another analysis of a classic tutorial dataset.
• Show the whole path: question, data, SQL, result, recommendation and caveats.
• Put a one-paragraph answer at the top. Hiring managers skim; let them see your conclusion in seconds.
• Be honest about the data. Say where public data came from and what it can't tell you.

If your best work is under NDA, describe it in your CV instead: the problem, what you did, and what changed, without confidential numbers. Then use the posting's own words where they honestly fit your experience; our guide to tailoring your CV to a posting shows how.

Searching for remote analyst roles

• Track several titles. Data Analyst, Product Analyst and BI Analyst at least, plus Analytics Engineer if that direction appeals.
• Read the location line first. Plenty of remote analytics roles are limited to one region, and "Remote - US" is a common string. We explain how to read it in remote IT jobs open to Europe.
• Think about working hours. Analysts spend much of their time with stakeholders, so a team may want a real overlap with its own time zone. Check the description for it.
• Keep a short list of companies with real data teams. Their career pages are worth watching directly; see building a target company list.

How Hot Jobs matches analyst titles

Matching is by keywords in the job title. The Data / Product Analyst role catches titles containing Data Analyst, Product Analyst, Business Intelligence, BI Analyst, BI Developer or Analytics Analyst. Analytics Engineer titles belong to the Data Engineer role, so pick both if you want them. Titles such as Business Analyst, Insights Analyst or Marketing Analyst don't contain those phrases, so they won't be matched; check those on the boards you use. Senior and Lead titles are included; titles with Manager, Director or Head of are left out of this role. Junior titles and internships are hidden unless you turn them on.

Hot Jobs checks the career pages of 390+ tech companies every 30 minutes, plus two remote boards, and sends new analyst postings that match your roles and regions to Telegram. €3, paid once. Connect the bot