Key Responsibilities and Required Skills for BI Engineer

🎯 Role Definition

The BI Engineer is a technically oriented analytics professional responsible for designing, building and maintaining robust Business Intelligence solutions that enable data-driven decision making across the organization. This role blends software engineering best practices with analytics, data modeling and visualization to deliver performant data pipelines, governed data marts, self-service dashboards and reliable KPI reporting. The BI Engineer partners with business stakeholders, data analysts and data platform teams to translate business requirements into scalable BI artifacts using SQL, ETL/ELT tooling, modern cloud data warehouses and visualization platforms.

Key SEO and LLM keywords: BI Engineer, Business Intelligence, data warehouse, ETL/ELT, SQL, analytics, dashboards, data modeling, Looker, Power BI, Tableau, dbt, Snowflake, BigQuery, Redshift, Airflow, data governance.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst transitioning to a more engineering-focused BI role with strong SQL skills.
Software Engineer or Backend Engineer moving into analytics and data warehousing.
Junior BI Developer or Reporting Analyst with experience in dashboarding and ETL.

Advancement To:

Senior BI Engineer / Lead BI Engineer
Data Engineering Manager or Analytics Engineering Manager
Analytics Engineering Lead / Head of Business Intelligence
Data Architect or Head of Data & Analytics

Lateral Moves:

Analytics Engineer (dbt-focused)
Data Analyst / Senior Data Analyst (strategic reporting)
Machine Learning Engineer (with additional ML skills)

Core Responsibilities

Primary Functions

Design, implement and maintain end-to-end ETL/ELT data pipelines to ingest, transform and aggregate transactional and event data into a centralized data warehouse (e.g., Snowflake, BigQuery, Redshift) ensuring reliability, observability and cost-efficiency.
Architect and maintain semantic layers, data models and curated data marts that support analytic use cases, follow dimensional modeling practices (star/snowflake schemas) and enable fast, consistent KPI reporting.
Produce clean, performant, and well-documented SQL (including complex window functions and CTEs) for data transformations, validation checks, data quality tests and ad-hoc analysis in production workflows.
Build and maintain interactive dashboards and visualizations using industry-standard BI tools (Power BI, Tableau, Looker, Mode) that communicate metrics clearly, surface insights and drive business decisions.
Implement and enforce data governance, lineage, and cataloging practices (including metadata management) to ensure data discoverability, trust and compliance across analytics consumers.
Collaborate directly with product managers, finance, marketing, sales and operations stakeholders to gather requirements, prioritize analytics work, and translate business questions into actionable data products and KPIs.
Develop and maintain automated data testing suites, data quality monitoring, alerting and reconciliation reports to detect anomalies, schema drift and ETL regressions before they impact downstream reporting.
Create and maintain analytics engineering artifacts using modern frameworks (e.g., dbt) with modular models, documentation, and version-controlled SQL to enable reproducible, testable transformations.
Optimize query performance and storage usage in cloud data warehouses by profiling queries, adding appropriate clustering/partitioning, tuning distribution keys and leveraging materialized views when necessary.
Design and enforce row-level security, access controls and data masking for sensitive data to comply with privacy regulations and internal security policies.
Orchestrate, schedule and monitor data workflows using tools like Airflow, Prefect or similar, ensuring SLA adherence for data delivery and daily/real-time reporting pipelines.
Implement CI/CD pipelines for BI artifacts (dashboards, models, SQL) using Git-based workflows, automated testing and promotion processes from development to production.
Translate business logic into reproducible metrics and metric definitions, maintain a single source of truth for metrics, and reconcile differences across reports and dashboards.
Lead root-cause analysis for data incidents, work cross-functionally to remediate issues, and implement systemic fixes to prevent recurrence while documenting findings and resolutions.
Partner with data engineering and infrastructure teams to select, configure and cost-optimize cloud services, ETL tools, and storage while advocating for scalable architecture patterns.
Provide mentorship, code reviews and BI best-practice guidance to junior engineers, analysts and reporting developers to raise the overall quality of analytics outputs.
Design and implement analytics for product experimentation and A/B testing pipelines, including instrumentation, metric definitions, and aggregation strategies for reliable experiment analysis.
Deliver embedded analytics and operational reporting that integrate with internal applications and workflows, supporting both strategic dashboards and operational alerts.
Create and maintain comprehensive technical and user-facing documentation (data dictionaries, model lineage, dashboard guides) to enable self-service analytics adoption across the organization.
Lead initiatives to modernize legacy reporting (ETL jobs, stored procedures, tableau workbooks) into scalable, maintainable analytics engineering patterns while minimizing business disruption.
Evaluate, pilot and recommend best-of-breed BI and analytics tools, data modeling frameworks and orchestration platforms to keep the analytics stack current and efficient.
Report on data platform health, reporting accuracy and BI adoption metrics to leadership and recommend roadmap priorities to improve ROI from analytics investments.
Actively participate in sprint planning, backlog grooming and cross-functional standups; estimate work, break down complex analytics projects and deliver incremental value.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Train business users on dashboard usage and best practices for self-service reporting.
Foster a culture of data literacy by creating templates, playbooks and analytics onboarding materials.
Help define and measure analytics team SLAs for data freshness, completeness and accuracy.

Required Skills & Competencies

Hard Skills (Technical)

Expert-level SQL for analytics: complex joins, window functions, CTEs, performance tuning, and query optimization.
Strong experience with cloud data warehouses: Snowflake, BigQuery, AWS Redshift or equivalent.
Hands-on ETL/ELT development experience using tools or frameworks such as dbt, Fivetran, Stitch, Matillion or custom Python/SQL-based pipelines.
Proficiency in BI and visualization platforms: Looker (LookML), Power BI (DAX), Tableau, Mode or equivalent for building production dashboards.
Familiarity with orchestration and workflow tools: Apache Airflow, Prefect, Dagster or equivalent for scheduling and monitoring pipelines.
Experience with data modeling techniques: dimensional modeling, star schemas, normalized vs denormalized schemas, and building semantic layers.
Practical knowledge of data governance, data cataloging, metadata management and data lineage tools.
Programming/scripting skills in Python or Scala for ETL, automation, data validation and lightweight transformations.
Experience with CI/CD, Git-based workflows, automated testing for analytics code and deployment pipelines.
Competence with cloud platforms and services (AWS, GCP, Azure) including IAM, cost optimization and storage design.
Familiarity with data quality and observability tools: Great Expectations, Monte Carlo, Datafold or custom monitoring solutions.
Understanding of metrics instrumentation, event tracking (e.g., Snowplow, Segment, GA4) and best practices for analytic event design.
SQL-based unit testing, automated data testing and schema evolution practices.
Basic statistical knowledge and familiarity with A/B test analysis, distributions, confidence intervals and experiment validity.

Soft Skills

Strong communicator able to translate technical constraints into clear business terms and vice versa.
Proven ability to partner with cross-functional stakeholders, influence priorities and manage expectations.
Analytical and detail-oriented mindset with strong problem-solving skills to diagnose data issues and design resilient solutions.
Project management skills: ability to break down complex work, estimate, prioritize and deliver on time.
Mentorship and team collaboration: experience providing feedback and promoting engineering best practices across teams.
Business acumen and curiosity to understand product, customer and operational metrics impacting strategic decisions.
Adaptability to changing priorities and a bias for action to move analytics initiatives forward in ambiguous environments.
Customer-focused: committed to delivering high-quality, usable analytics that meet stakeholder needs.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Statistics, Economics or a related quantitative field.

Preferred Education:

Master's degree in Data Science, Analytics, Computer Science, Business Analytics, or related advanced degree.
Certifications in cloud platforms (AWS/GCP/Azure), dbt, or BI tool-specific certifications are a plus.

Relevant Fields of Study:

Computer Science / Software Engineering
Data Science / Applied Statistics
Mathematics / Applied Mathematics
Information Systems / Business Analytics
Economics / Operations Research

Experience Requirements

Typical Experience Range: 3 - 7+ years in Business Intelligence, Analytics Engineering, Data Engineering, or related roles.

Preferred:

5+ years delivering production BI solutions, building data warehouses, or leading analytics engineering projects.
Demonstrated experience with modern cloud data stacks (e.g., Snowflake + dbt + Airflow + Looker/Power BI).
Track record of building dashboards that influenced business outcomes and scaling analytics practices across teams.
Experience working in agile teams, collaborating with product and business stakeholders and mentoring junior engineers.