Job description template

Business Intelligence Analyst

We’re seeking a Business Intelligence Analyst to turn data into clear, decision-ready insights. You’ll build and maintain reporting and analytics that support key business processes, partnering with stakeholders to define metrics, data definitions, and performance reporting.

Responsibilities

Develop and maintain BI dashboards and recurring reports for business stakeholders
Translate stakeholder questions into analytical requirements, including metric definitions and success criteria
Build and maintain datasets supporting reporting (e.g., sourcing, transformations, data quality checks)
Perform exploratory analysis to identify trends, drivers, and opportunities across performance metrics
Create and document KPI/metric definitions to ensure consistent reporting across teams
Support data-driven planning by producing ad hoc analyses when priorities change
Monitor reporting reliability and investigate data discrepancies to improve trust in analytics

Must-have qualifications

Hands-on experience building BI dashboards and recurring reporting in a production environment
Strong SQL skills for data extraction, transformation, and analysis
Experience defining and maintaining KPI/metric definitions for consistent reporting
Proficiency with data visualization principles to present insights clearly to non-technical stakeholders
Ability to validate data quality and trace discrepancies to root causes
Experience working with relational databases and/or data warehouse data models
Comfort partnering with stakeholders to gather requirements and iterate on reporting needs

Nice to have

Experience with semantic layer concepts (e.g., metric layers) or governed data definitions
Experience with Python for data analysis or automation of BI workflows
Familiarity with Looker Studio/Looker, Tableau, Power BI, or similar BI tools
Experience implementing data quality checks and monitoring for analytics

What success looks like in 12 months

Stakeholder-ready reporting is consistently delivered
KPI definitions are standardized and adopted
Data accuracy and reliability improve
Analytical insights drive measurable decisions
Analytics requirements and documentation are complete and maintainable

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

Deliver trusted, decision-ready analytics by building scalable reporting and defining KPIs that stakeholders can consistently act on.

Outcomes to measure

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

Stakeholder-ready reporting is consistently delivered. Measure: Create and maintain [N] dashboards/reports with documented owners and SLAs; achieve [X]% stakeholder-reported usability/clarity in reviews
KPI definitions are standardized and adopted. Measure: Publish and socialize KPI/metric definitions for [N] core metrics; reach [X]% adoption across requesting teams by usage of the agreed definitions
Data accuracy and reliability improve. Measure: Reduce reporting discrepancies/incidents by [X]% through root-cause fixes and data quality checks
Analytical insights drive measurable decisions. Measure: Produce [N] analytics deliverables per quarter (or [N] total) that result in at least [X] documented action items (e.g., plan changes, prioritization, or experiments) linked to the analysis
Analytics requirements and documentation are complete and maintainable. Measure: Maintain requirement and documentation artifacts for [N] reporting areas; achieve [X]% reduction in rework requests caused by unclear requirements/definitions

Competencies and weights

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

SQL for Analysis · 22%
Core to extracting, transforming, and validating data for dashboards and analyses. Evaluated in Round 1.

Weak: Writes basic queries but struggles to join multiple tables, handle edge cases, or explain results clearly.
Meets the bar: Builds correct SQL for analysis and reporting; handles joins/aggregations and explains query logic and assumptions.
Strong: Designs efficient SQL with clear logic, robust handling of edge cases, and demonstrates repeatable patterns for reconciliation and validation.

BI Dashboard Delivery · 18%
Direct responsibility for producing dashboards and recurring reports that stakeholders use. Evaluated in Round 2.

Weak: Creates reports that lack clarity, structure, or consistency; requires frequent rework to match needs.
Meets the bar: Builds dashboards with clear layouts, filters, and metric alignment; iterates based on feedback to improve usefulness.
Strong: Delivers well-structured dashboards with strong metric alignment, thoughtful interactivity, and proactive improvements to usability and maintainability.

KPI Definition Stewardship · 16%
Ensures consistent metric definitions across teams and prevents conflicting reporting. Evaluated in Round 1.

Weak: Defines metrics ambiguously or changes definitions without stakeholder alignment.
Meets the bar: Creates clear KPI documentation (logic, grain, ownership) and ensures stakeholders understand the definitions.
Strong: Leads alignment across stakeholders, resolves conflicting interpretations, and maintains versioned definitions with traceability.

Data Quality and Reconciliation · 16%
Analytics value depends on trust; the role must identify and fix discrepancies. Evaluated in Round 2.

Weak: Notices data issues late or cannot trace issues to source; relies on guesswork.
Meets the bar: Uses validation checks and reconciliation steps to find likely causes of discrepancies and proposes fixes.
Strong: Establishes systematic quality checks/controls, identifies root causes reliably, and prevents recurrence through durable fixes.

Stakeholder Requirements · 15%
Success depends on understanding needs, translating them into analytical requirements, and iterating. Evaluated in Round 3.

Weak: Collects requirements incompletely and struggles to confirm understanding; changes scope without clarity.
Meets the bar: Asks clarifying questions, summarizes requirements, and iterates reports based on stakeholder feedback.
Strong: Proactively structures ambiguous problems into measurable requirements, aligns stakeholders early, and drives effective decision-making through clear communication.

Analytical Insight Communication · 10%
Converts analysis into clear narratives and decision-ready recommendations. Evaluated in Round 3.

Weak: Presents data without a clear takeaway; omits assumptions and limitations.
Meets the bar: Communicates key findings, supports them with evidence, and explains assumptions/limitations.
Strong: Tells a crisp story that connects drivers to outcomes, quantifies impact, and recommends next steps with confidence levels.

Continuous Improvement Mindset · 3%
Supports maintainability and ongoing refinement of reporting systems. Evaluated in Round 2.

Weak: Waits for issues to surface before improving; documentation is inconsistent.
Meets the bar: Improves reports and documentation based on feedback and observed issues; keeps artifacts up to date.
Strong: Systematically identifies recurring problems, proposes improvements, and elevates standards for others to follow.

Interview questions

Questions for every interview round

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

Round 1: Data & KPI Foundations
Analytics Lead · 45 min

Tell us about a time you defined KPIs for a stakeholder group. How did you decide the metric logic, grain, and inclusion/exclusion rules?
Walk through a SQL query you wrote for reporting: what problem did it solve, what tables did you join, and how did you validate correctness?
Describe a situation where different teams interpreted the same metric differently. What steps did you take to align on a single definition?
How do you approach investigating an unexpected change in a KPI trend (e.g., sudden drop/spike)?

Round 2: BI Delivery & Data Reliability
BI Engineering Manager · 50 min

Describe a dashboard or recurring report you built. What design choices improved clarity for non-technical stakeholders, and how did you gather/act on feedback?
Tell us about a data discrepancy you encountered in reporting. What checks did you perform to reconcile results and what was the root cause?
Share an example of how you improved data quality for analytics (e.g., added validation checks, corrected transformations, or improved upstream logic).
How do you ensure that dashboards stay maintainable as requirements evolve (new filters, metrics, or segmentations)?

Round 3: Stakeholders & Decision Impact
Product/Operations Stakeholder · 55 min

Tell us about a time you translated a stakeholder question into an analytical requirement. How did you confirm you understood the ask and what success looked like?
Describe an ad hoc analysis you performed. What was the key insight, what evidence supported it, and how did you communicate limitations/assumptions?
Give an example of a time your analysis influenced a decision. What action was taken, how was it measured, and what would you do differently next time?
When stakeholders request changes to existing reporting, how do you manage scope, versioning, and communication to avoid breaking trust in metrics?

Red flags to watch for

Relies on manual spreadsheets or undocumented logic for “final” reporting instead of building repeatable, validated datasets
Cannot explain SQL logic at a level that supports correctness, assumptions, and reconciliation
Demonstrates unclear or inconsistent KPI definitions, leading to conflicting stakeholder reporting
Ignores data quality problems or treats discrepancies as unavoidable without a root-cause approach
Frequently communicates insights without context, limitations, or evidence to support decisions

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

In 2026, a senior Business Intelligence 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.

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Business Intelligence Analyst Job Description Template