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Data Analyst
Free template · 2026

Data Analyst Job Description Template

A ready-to-use Data Analyst job description for 2026, with the interview scorecard and questions to hire one. Copy it as is or customize it free in about 15 seconds.

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Includes the job description, an interview scorecard and questions for every round.

Mid-level in Latin America
$3,250/mo
Same role in the US
$6,625/mo
You save
Up to 51%

All-in monthly cost, 2026 rates.

Job description template
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Data Analyst

We’re hiring a Mid-level Data Analyst to turn messy data into clear insights that drive product, operations, and customer decisions. You’ll own analysis end-to-end—from defining questions and metrics to building reliable reporting and communicating findings to stakeholders.

Responsibilities

  • Define analytical questions with stakeholders and translate them into measurable metrics
  • Build and maintain dashboards and reporting that support ongoing business decisions
  • Perform data preparation, quality checks, and exploratory analysis to diagnose drivers of key metrics
  • Design and run analysis to evaluate performance, identify trends, and quantify impact of changes
  • Create clear written and verbal summaries of findings, assumptions, and recommendations
  • Collaborate with engineering and other teams to improve data quality, schemas, and metric definitions
  • Document methodologies and maintain reusable analysis assets for repeatability

Must-have qualifications

  • Hands-on experience with SQL for analysis and reporting across production datasets
  • Experience building dashboards or recurring reports using BI tools (e.g., Looker, Tableau, Power BI)
  • Proficiency in data cleaning and validation (missing data, outliers, joins, deduplication)
  • Ability to define and maintain metric logic and data definitions with consistency
  • Strong statistical reasoning to interpret results and communicate uncertainty
  • Experience with Python or another scripting language for data analysis and automation

Nice to have

  • Experience with event-based or behavioral analytics (funnels, cohorts, retention)
  • Familiarity with data modeling concepts (fact/dimension, star schema) and analytics-friendly transformations
  • Experience working with A/B testing or experimentation analysis

What success looks like in 12 months

  • Reliable metric definitions and reporting foundation
  • Faster insight delivery for key questions
  • Improved data quality signals for analytics
  • Actionable insights tied to measurable impact
  • Documentation and reusability of analysis assets

Logistics

  • Full-time (40 hours a week), remote from Latin America.
  • Working hours overlap with US business hours.

About the company

[About your company: what you build, your stage and team size]

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Interview scorecard

Score every candidate against the same bar

Enable data-driven decisions by delivering accurate, well-defined metrics and actionable analysis that stakeholders trust.

Outcomes to measure

What this hire should deliver in the first year. Replace [N] with your own targets.

  • Reliable metric definitions and reporting foundation. Measure: Publish and get stakeholder sign-off on [N] core metric definitions, ensuring dashboards/queries match the agreed logic with <[X]% discrepancy on audited samples
  • Faster insight delivery for key questions. Measure: Reduce time-to-deliver for assigned analysis from intake to first recommendation by [X]% compared to the initial baseline measured in the first [Y] weeks
  • Improved data quality signals for analytics. Measure: Implement [N] data quality checks (e.g., null rate, freshness, duplicate detection) and reduce recurring reporting issues by [X]%
  • Actionable insights tied to measurable impact. Measure: Produce [N] stakeholder-facing analyses with documented decision/recommendation and measurable follow-through (e.g., implemented change or tracked experiment), achieving [X]% of analyses reaching a defined “used” outcome
  • Documentation and reusability of analysis assets. Measure: Create and maintain [N] reusable templates (dashboard views, SQL patterns, or analysis notebooks) with documented assumptions and usage notes for handoffability

Competencies and weights

What to evaluate, how much it counts and what a strong answer looks like.

SQL for analysis · 25%
SQL is the primary tool for extracting, transforming, and validating data used in reporting and analysis. Evaluated in Round 1.

  • Weak: Writes queries that frequently require rework due to incorrect joins, filters, or aggregations; misses obvious data-quality issues.
  • Meets the bar: Builds correct SQL queries for defined metrics and validates results with sanity checks.
  • Strong: Creates clear, maintainable SQL with robust validation (row counts, uniqueness checks, edge-case handling) and explains metric logic accurately.

Metric definition rigor · 20%
Consistent metric logic and definition alignment reduce confusion and prevent conflicting interpretations. Evaluated in Round 2.

  • Weak: Defines metrics informally or changes logic without clear communication to stakeholders.
  • Meets the bar: Documents metric definitions and aligns logic with stakeholders, including clear assumptions.
  • Strong: Leads metric alignment by reconciling competing definitions, establishing single sources of truth, and maintaining consistency over time.

BI dashboards reporting · 15%
The role must deliver trustworthy dashboards and repeatable reporting stakeholders rely on. Evaluated in Round 2.

  • Weak: Builds dashboards that break easily or do not align with agreed metric definitions.
  • Meets the bar: Develops dashboards/reports that correctly reflect agreed definitions and are easy for stakeholders to interpret.
  • Strong: Designs dashboard experiences with thoughtful filters/segmentations, consistent metric presentation, and documented caveats.

Data quality validation · 15%
Accurate decisions require strong data cleaning, validation, and reconciliation practices. Evaluated in Round 1.

  • Weak: Overlooks data-quality problems (duplicates, join inflation, missingness) or only catches them after stakeholders report issues.
  • Meets the bar: Performs structured checks for data correctness and reconciles results across sources.
  • Strong: Proactively builds validation steps and documentation to prevent recurring issues and clearly communicates impact of data anomalies.

Statistical reasoning · 15%
Interpreting results correctly (including uncertainty) is essential for actionable recommendations. Evaluated in Round 3.

  • Weak: Draws conclusions without checking assumptions or acknowledging uncertainty; misinterprets variability.
  • Meets the bar: Applies appropriate statistical techniques for common analysis questions and communicates limitations clearly.
  • Strong: Selects suitable methods for the problem, quantifies uncertainty, and recommends next steps based on statistical strength.

Stakeholder communication · 10%
Insights must be understood and acted on; this requires clear communication of findings, assumptions, and recommendations. Evaluated in Round 3.

  • Weak: Presents results without clear context, assumptions, or decision relevance; stakeholders struggle to act.
  • Meets the bar: Explains analysis purpose, methodology, results, and recommendations clearly and in stakeholder language.
  • Strong: Tailors communication for different audiences, anticipates questions, and drives decisions through clear narratives and next-step framing.
Interview questions

Questions for every interview round

Each round has an owner, a time box and questions that test the competencies above.

Round 1: SQL & data validation
Data Analytics Lead · 45 min

  1. Walk through how you would write an SQL query to calculate a metric end-to-end (source selection, joins, deduplication, filters, and aggregation). How do you validate the result?
  2. Describe a time you found a data issue caused by join behavior (e.g., join inflation or missing keys). What checks did you run and how did you fix the logic?
  3. How do you approach exploratory analysis to identify outliers, missingness, and unexpected distribution shifts before building dashboards?

Round 2: Dashboards & metric definitions
BI/Analytics Manager · 45 min

  1. Tell us about a dashboard or recurring report you built. What questions did it answer, what metrics did it include, and how did you ensure it stayed aligned with metric definitions?
  2. Describe your process for defining and documenting metric logic. How do you handle conflicting stakeholder definitions or edge cases?
  3. When you update underlying logic or source tables, what steps do you take to prevent breaking changes or mismatches for users?

Round 3: Insights & statistical reasoning
Product Analytics Partner · 45 min

  1. Describe an analysis where you used statistics to support a conclusion. What method did you choose and how did you communicate uncertainty or limitations?
  2. Give an example of an insight you produced that led to a decision or measurable impact. How did you quantify impact and ensure stakeholders trusted the result?
  3. How do you structure stakeholder communication for a complex analysis (context, assumptions, results, recommendation)? Provide an example of how you handled tough questions or skepticism.

Red flags to watch for

  • Uses SQL superficially (e.g., cannot explain join/aggregation choices or validation steps) leading to unreliable metrics
  • Treats metric definitions as informal or changes logic without documentation or stakeholder alignment
  • Overlooks statistical uncertainty or interprets analysis results as definitive without acknowledging limitations
  • Relies on ad-hoc analyses without validation, documentation, or repeatability for ongoing reporting

How much does it cost to hire a Data Analyst in Latin America?

In 2026, a senior Data Analyst in Latin America costs $4,875 to $5,625 a month all-in, versus $9,625 in the US: up to 49% less. Mid-level starts at $3,250.

Country

Mid-level

Senior

Argentina

$3,250

$4,875

Brazil

$3,625

$5,375

Colombia

$3,250

$4,875

Mexico

$3,750

$5,625

United States

$6,625

$9,625

Monthly cost in USD, all-in: the talent's salary plus Teilur's flat 20% fee, from our 2026 rates. The United States row shows what the same role typically costs there.

Compare costs in the calculator →

Frequently asked questions

What does a Data Analyst do?

Typical responsibilities include: define analytical questions with stakeholders and translate them into measurable metrics; build and maintain dashboards and reporting that support ongoing business decisions; perform data preparation, quality checks, and exploratory analysis to diagnose drivers of key metrics. The full list is in the template above.

How much does it cost to hire a Data Analyst in Latin America?

In 2026, a senior Data Analyst in Latin America costs $4,875 to $5,625 a month all-in, versus $9,625 in the US: up to 49% less. Mid-level starts at $3,250.

Can I customize this Data Analyst job description?

Yes. Teilur's free Job Description & Scorecard Generator rewrites it for your stack, seniority, time zone and hiring urgency in about 15 seconds. No sign-up needed.

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