DataHire logo

Data Hire

Data Analyst Jobs

Career Resources

A curated, practical guide to help you land (and excel in) Data Analyst roles—whether you're just getting started or stepping up to senior.

Table of Contents

  1. How to Use This Page
  2. Career Paths & Levels
  3. Skills Map
  4. Tooling Guide
  5. Portfolio Playbook
  6. Resume & LinkedIn Templates
  7. Cover Letter Template
  8. Interview Prep
  1. Take‑Home Assignment Guide
  2. Offer, Salary & Negotiation
  3. Job Search Workflow
  4. Networking That Works
  5. 30/60/90 Day Plan
  6. Specialized Tracks
  7. Data Ethics & Privacy
  8. FAQ

Tip: Use the checklists as your weekly ritual.

How to Use This Page

  • Pick a lane & level. Then follow the Skills Map.
  • Build 2–3 portfolio projects that mirror real job postings.
  • Apply weekly with intent (targeted roles + tailored resume bullets).
  • Practice interviews using the question banks and case templates.
  • Track everything (applications, outreach, interview notes).

Career Paths & Levels

Paths

  • Business/Data Analyst (Generalist): Reporting, dashboards, ad‑hoc analysis.
  • Product Analyst: Experimentation, product metrics, funnels, feature impact.
  • Marketing/Growth Analyst: Attribution, channel performance, cohort/LTV.
  • Operations/Supply Chain Analyst: Throughput, forecasting, logistics.
  • Financial/Data Analyst: Revenue/COGS, variance, driver‑based planning.
  • People/HR Analyst: Retention, performance, headcount analytics.
  • Geospatial Analyst: Location data, routing, catchment analysis.

Levels (signals)

  • Entry/Junior: Solid Excel/Sheets, SQL CRUD & joins, a dashboard, 2 projects.
  • Mid: SQL window functions, data modeling basics, Python/R for EDA, A/B test design, stakeholder comms.
  • Senior: End‑to‑end from problem framing to recommendation; owns KPIs, mentors others, productionizes reporting, partners cross‑functionally.

Skills Map (What to Learn, In What Order)

Foundation

  1. Spreadsheet fluency (lookup/index-match/xlookup, pivots, arrays, regex, charts)
  2. SQL core (SELECT, WHERE, GROUP BY, HAVING, ORDER, LIMIT)
  3. Joins & set ops (INNER/LEFT/RIGHT/FULL, UNION/EXCEPT/INTERSECT)
  4. Aggregations & windows (ROW_NUMBER, RANK, SUM/AVG OVER, partitions)
  5. Data cleaning (dedupe, outliers, nulls, date/time handling)

Analysis Toolkit

  1. Descriptive stats (mean/median, variance, CI, percentiles)
  2. Experimentation (A/B, power, lift, significance, pitfalls)
  3. Cohorts & funnels (retention, conversion stages, drop‑off)
  4. Forecasting basics (seasonality, moving averages, simple regressions)
  5. Visualization design (chart choice, clutter reduction, labeling)

Automation & Production

  1. Python or R for EDA & scripting (pandas/dplyr, matplotlib/ggplot)
  2. BI tools (Tableau/Power BI/Looker/Mode/Superset) & dashboard craft
  3. SQL modeling (CTEs, materialized views, star schemas, date dimensions)
  4. Version control (Git basics) & reproducibility (notebooks, dbt basics)

Soft Skills

  1. Framing questions, stakeholder interviews, storytelling, writing clear TL;DRs.

Shortcut: Learn just enough to ship—then iterate in public on your portfolio.

Tooling Guide

Spreadsheets

Must‑know: Pivot Tables, XLOOKUP/INDEX‑MATCH, TEXT/DATE functions, FILTER/UNIQUE, conditional formatting, Named Ranges.

SQL

  • Practice: joins, windows, CTEs, date bucketing, CASE, subqueries, pivot/unpivot.
  • Performance: EXPLAIN basics, limiting scans, pre‑aggregations, proper indexing (where applicable).

Python/R

  • Python: pandas, numpy, matplotlib; notebooks with tidy headings; virtual envs.
  • R: tidyverse (dplyr, tidyr, ggplot2); RMarkdown for reports.

BI/Dashboarding

  • Principles: 1‑page KPI view, consistent filters, responsive layouts, drill‑downs.
  • Governance: single source of truth, definitions dictionary, refresh cadence.

Versioning & Reproducibility

  • Git workflow: branch → commit → PR; write concise commit messages.
  • Project structure: /data, /notebooks, /src, /reports; README with how‑to‑run.

Portfolio Playbook

Your goal: Prove you can produce business impact in the employer's stack.

Signature Projects (pick 2–3)

  1. 1. Business KPI Dashboard – Ingest (csv/api), model with SQL, build a BI dashboard. Include refresh plan and a short Loom‑style walkthrough.
  2. 2. A/B Test Deep‑Dive – Simulate or analyze an experiment; cover design, power, sanity checks, lift calc, and clear recommendation.
  3. 3. Cohort Retention Analysis – Define cohorts, compute retention curves, segment, identify drivers, propose experiments.
  4. 4. Revenue & Margin Bridge – Decompose change over time (price, volume, mix) and present a waterfall chart with insights.
  5. 5. Supply Chain/Inventory Forecast – Create a simple forecasting model with seasonality and service‑level scenarios.

Deliverables Checklist

  • • Git repo (or clean Google Drive folder) with reproducible steps
  • • Executive summary (≤200 words) + slides (5–8 pages) + BI link
  • • Data dictionary & metric definitions
  • • Readme: how to run, assumptions, limitations

Public Proof

Post short write‑ups, visuals, and lessons learned on LinkedIn/Twitter.

Resume & LinkedIn Templates

Resume (1 page)

  • Header: Name · Email · City (or Remote) · Portfolio/LinkedIn/GitHub
  • Summary (2–3 lines): Your impact, toolset, domains.
  • Skills: SQL · Excel/Sheets · Python/R · Tableau/Looker/Power BI · A/B Testing · Statistics · Data Modeling · Communication
  • Experience: 3–5 bullets per role, each with Action → Method → Impact and a number.

Bullet formula examples

"Built a weekly revenue dashboard in Looker (SQL + derived tables) used by Sales; reduced time‑to‑insight by 70% and surfaced a 5% upsell opportunity."

"Designed checkout A/B (power=0.8); variant increased conversion +1.9% (p=0.03) → $240k incremental ARR."

LinkedIn

  • Headline: "Data Analyst • SQL | Python | Tableau • Experimentation & Growth"
  • About: 3–5 short paragraphs; link 2–3 projects; list domains (e.g., ecommerce, fintech).

Cover Letter Template

Opening (2–3 lines):

I'm a Data Analyst with experience in [domain] who loves turning messy data into clear decisions. I'm excited about [company] because [specific reason/tie to mission/product].

Middle (3 bullets):

  • • Recent win: [one quantified impact].
  • • Relevant project: [portfolio link + 1‑line business outcome].
  • • Tool/stack match: [their posting says X; you have X].

Close (1–2 lines):

I'd love to share how I can help [team] hit [KPI/goal]. Thanks for your time.

Interview Prep

A. Recruiter/Screen (10–20 min)

  • • Crisp story: who you are, top 2–3 wins, tools, domains, why this company.
  • • Be ready with compensation range and timeline.

B. SQL/Technical (45–60 min)

  • Topics: joins, windows, CTEs, date bucketing, conditional aggregation, top‑N, percentiles.
  • Question pattern:
    • • "Find 7‑day rolling active users per product."
    • • "Return top 3 categories by monthly revenue growth."
  • Checklist: ask clarifying Qs, sketch schema, write readable SQL (CTEs), test with edge cases.

C. Analytics/Case (45–60 min)

Framework (4 steps):

  1. 1. Clarify the goal & metric(s)
  2. 2. Hypothesize drivers & segments
  3. 3. Plan analysis (cuts, visuals, tests)
  4. 4. Recommend action + success metric

Sample prompts: "Sign‑ups fell 10% WoW," "Cart‑to‑purchase lags," "New feature—how to measure impact?"

D. BI/Visualization (30–45 min)

Give a before/after dashboard: declutter, better chart choice, narrative title, callouts.

E. Stakeholder/Behavioral

STAR your stories (Situation, Task, Action, Result). Prepare 6 stories: conflict, impacting a KPI, mistake/learning, influencing without authority, ambiguous ask, urgent deadline.

Take‑Home Assignment Guide (and Rubric)

What good looks like

  • • Clear README with business context, assumptions, and reproducible steps.
  • • Clean SQL/Python notebooks; thoughtful visuals; concise executive summary.
  • • Recommendations with trade‑offs, risks, and next steps.

Self‑Score Rubric (0–3)

  • Business Understanding: 0 (none) → 3 (sharp framing, measurable goal)
  • Data Hygiene: 0 (messy) → 3 (validated, edge cases handled)
  • SQL/Code Quality: 0 (hard to read) → 3 (modular, tested)
  • Insights & Storytelling: 0 (descriptive only) → 3 (actionable drivers)
  • Visualization: 0 (clutter) → 3 (clean, labeled, purposeful)
  • Reproducibility: 0 (can't run) → 3 (one‑click/clear steps)

Offer, Salary & Negotiation

Preparation

  • • Research band ranges by level, location, and company size; note base + bonus + equity.
  • • Identify your walk‑away and ideal numbers; list trade‑offs (remote, growth, title).

Negotiation script

"I'm excited about the role. Based on the scope and market data for [city/level], I'm targeting a total range of [X–Y]. Is there flexibility on base or equity to get closer to that?"

Levers

Start date, sign‑on bonus, performance review timing, remote stipend, L&D budget.

Networking That Works

Warm outreach (ex‑colleagues, alumni):

"Could I get 15 minutes for advice on your team's analyst role? I built a similar dashboard here [link] and would value your perspective."

Cold outreach

"Saw you lead analytics at [Company]. I built a [relevant] project that mirrors your stack. If helpful, I can share a 5‑minute walkthrough—open to a quick chat?"

Events & communities

Contribute answers, share dashboards, offer quick analyses that help others.

30/60/90 Day Plan (Once You're Hired)

  • Days 0–30: Meet stakeholders; map metrics and data sources; fix a nagging reporting pain; ship a small dashboard.
  • Days 31–60: Own a KPI; create definitions; automate a weekly report; document a data contract.
  • Days 61–90: Propose an experiment or roadmap; mentor a junior; present a QBR‑style insights deck.

Specialized Tracks

Product Analytics

  • Metrics: activation, retention, feature adoption, North Star.
  • Tools: event tracking, experiment platforms.

Marketing Analytics

MMM vs MTA basics, cohorts, CAC/LTV, incrementality tests.

Finance Analytics

Driver trees, variance analysis, scenario planning.

Ops/Supply Chain

Forecast accuracy, inventory turns, SLA adherence, route optimization.

Geospatial

Spatial joins, clustering, isochrones; maps that tell a story (not just pretty).

Data Ethics & Privacy Basics

  • • Minimize PII, use least‑privilege access, log data lineage.
  • • Understand high‑level obligations (consent, purpose limitation, retention).
  • • Sanity‑check bias, fairness, and explainability in decisions.

FAQ

Q: Do I need both Python and R?

A: No. Pick one and get effective. Python is more common; R shines for quick stats and visuals.

Q: How many projects are enough?

A: Two excellent, business‑oriented projects beat ten generic ones.

Q: I get stuck in SQL interviews—now what?

A: Practice with realistic schemas, time yourself, and always narrate your approach.

Q: I have no experience—how do I start?

A: Volunteer a small analysis for a local org/startup, ship a KPI dashboard, and write up the impact.