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Key Responsibilities and Required Skills for Business Intelligence Engineer

💰 $85,000 - $150,000

DataAnalyticsBusiness IntelligenceEngineering

🎯 Role Definition

The Business Intelligence (BI) Engineer designs, builds and maintains the data infrastructure and analytics layer that enables business users to make data-driven decisions. This role focuses on end-to-end analytics delivery: extracting and transforming data, creating reliable data models and semantic layers, developing self-service dashboards and reports, and ensuring data quality, performance and governance across the analytics stack. A BI Engineer partners closely with product managers, analytics, data science and engineering teams to translate business requirements into scalable data solutions and repeatable analytics workflows.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning to engineering-focused analytics work
  • BI Analyst or Reporting Analyst with experience building dashboards and SQL models
  • Software Engineer or ETL Developer moving into analytics engineering

Advancement To:

  • Senior Business Intelligence Engineer / Analytics Engineer
  • Analytics Manager / BI Team Lead
  • Data Engineering Manager or Head of Analytics Engineering
  • Product Analytics Lead or Director of Data & Insights

Lateral Moves:

  • Data Engineer (focused on ingestion and infrastructure)
  • Data Scientist (modeling and advanced analytics)
  • Product Manager for analytics products or reporting platforms

Core Responsibilities

Primary Functions

  • Design, build and maintain robust ETL/ELT pipelines to ingest, cleanse and transform large-scale data from multiple source systems (SaaS, databases, event streams) into a centralized data warehouse (e.g., Snowflake, BigQuery, Redshift), ensuring accuracy, observability and cost-efficient processing.
  • Develop and maintain dimensional data models and semantic layers (star schemas, slowly changing dimensions, fact tables) that provide a consistent single source of truth for reporting and analytics across lines of business.
  • Author and optimize complex SQL queries and data models for performance, reliability and maintainability; implement indexing, partitioning and clustering strategies where applicable.
  • Build and ship high-quality, interactive business dashboards and operational reports using BI tools such as Looker, Tableau, Power BI or Qlik; iterate based on stakeholder feedback and usage analytics.
  • Implement analytics engineering best practices using tools like dbt (data build tool) for modular, tested, documented and version-controlled transformations.
  • Collaborate with product, finance, operations and marketing stakeholders to translate ambiguous business questions into measurable KPIs, metrics definitions and instrumentation plans.
  • Implement data validation, reconciliation and automated testing frameworks (unit tests, integration tests, data quality checks) to prevent regressions and detect anomalies in pipelines and models.
  • Establish and maintain monitoring, alerting and incident response for data pipelines, dashboards and query performance (using tools such as Airflow, Dagster, Prometheus, DataDog).
  • Maintain and enhance the analytics semantic layer (LookML, Power BI semantic models, Cube.js, or a metrics layer) to ensure metric consistency and reduce duplication across dashboards and reports.
  • Lead or participate in data governance initiatives including lineage documentation, cataloging, access controls, GDPR/CCPA compliance, and best-practice policies for sensitive and PII data handling.
  • Partner with data engineering and platform teams to optimize data ingestion patterns, storage design and compute costs in cloud environments (AWS, GCP, Azure) including cost monitoring and optimization strategies.
  • Instrument and validate event tracking and telemetry across web and mobile products; own the mapping from raw events to derived metrics and product funnels.
  • Implement and maintain CI/CD pipelines for analytics code, including automated dbt runs, model deployments and dashboard migrations to production.
  • Conduct performance tuning for slow-running queries and BI dashboards, including query rewriting, materialized views, incremental models and caching strategies to improve user experience.
  • Create comprehensive technical and user-facing documentation: data dictionaries, model lineage, dashboard guides and runbooks for operational processes.
  • Lead cross-functional analytics projects from requirements to production, including prioritization, scoping, milestone planning and stakeholder communication.
  • Provide mentoring, code reviews and onboarding for junior analytics engineers, analysts and new team members, promoting engineering rigor and analytics best practices.
  • Drive adoption of self-service analytics by creating templates, shared metrics libraries and training sessions for business users to explore and interpret data autonomously.
  • Evaluate, recommend and pilot new analytics tools, metrics layers, metadata and observability platforms to continuously improve the analytics stack and developer productivity.
  • Manage data access, user provisioning and role-based permissions for BI tools and the data warehouse to maintain security and least-privilege access.
  • Collaborate with machine learning and data science teams to productionize derived features and ensure alignment between feature stores, experimentation metrics and reporting.
  • Translate business requests into technical specifications, producing clear backlog tickets, acceptance criteria and test plans for the data engineering team.
  • Conduct root cause analysis for data incidents, lead remediation efforts and implement preventative measures to avoid recurrence.
  • Standardize naming conventions, metric definitions and reporting templates to reduce ambiguity and streamline onboarding for new teams and partners.
  • Support quarterly business reviews and executive reporting by curating dashboards, preparing narratives around metrics trends and responding to ad-hoc executive queries.

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 interpreting analytics.
  • Help define SLAs for data delivery and coordinate with engineering teams to meet them.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: ability to write, optimize and refactor complex queries, window functions, CTEs and analytical aggregations for performance and clarity.
  • Data Warehousing: hands-on experience designing and operating cloud data warehouses (Snowflake, Google BigQuery, Amazon Redshift) and storage optimization techniques.
  • ETL / ELT Tools & Orchestration: practical use of ETL/ELT tools and workflows (Fivetran, Stitch, Airflow, Prefect, Dagster) and experience scheduling and monitoring jobs.
  • Analytics Engineering (dbt): building modular SQL models, tests, documentation and CI workflows using dbt or equivalent transformation frameworks.
  • BI Platforms: designing and delivering dashboards in Looker (LookML), Tableau, Power BI, Qlik or similar tools with strong UX and performance sensibilities.
  • Data Modeling: dimensional modeling, star schemas, slowly changing dimensions, event modeling and schema evolution strategies.
  • Programming: scripting and automation experience in Python (pandas, SQLAlchemy), and familiarity with Git for version control and collaboration.
  • Cloud & DevOps Basics: understanding of cloud compute/storage, IAM, cost controls and infrastructure-as-code concepts (AWS/GCP/Azure).
  • Monitoring & Observability: experience setting up alerts and dashboards for pipeline health, SLAs, and query performance (Prometheus, DataDog, Grafana).
  • Data Quality & Testing: implementing data validation frameworks, unit/integration tests and reconciliation processes to ensure trustworthy analytics.
  • Metadata & Governance: experience with data catalogs, lineage tools, GDPR/CCPA considerations and role-based access control for data and BI artifacts.
  • Performance Tuning: ability to analyze query plans, optimize resource usage, and implement materialized views or incremental models to improve latency.
  • API & Event Instrumentation: familiarity with REST APIs, JSON, tracking libraries and event-schema design for accurate upstream instrumentation.
  • SQL-based Metrics Layer: building reusable, documented metric definitions or semantic layers to support consistent enterprise reporting.
  • Basic statistics and analytics fundamentals: understanding of hypothesis testing, cohorts, segmentation and A/B testing metrics interpretation.

Soft Skills

  • Business acumen: translate product and business goals into measurable KPIs and actionable analytics deliverables.
  • Stakeholder management: manage expectations, prioritize conflicting requests, and communicate status with non-technical audiences.
  • Clear written and verbal communication: write precise documentation, data dictionaries and present findings to cross-functional teams and executives.
  • Problem-solving and root-cause analysis: methodically break down issues, reproduce data problems, and recommend fixes and preventative controls.
  • Collaboration and teamwork: work closely with analysts, data scientists, engineers and product owners in a cross-functional environment.
  • Ownership and accountability: take end-to-end responsibility for analytics solutions from design through monitoring and iteration.
  • Adaptability: comfortable working in ambiguous environments and rapidly changing product requirements.
  • Mentoring and coaching: grow the analytics culture by guiding junior colleagues and sharing best practices.
  • Prioritization and time management: balance maintenance, feature work and ad-hoc business requests effectively.
  • UX and design sensitivity: craft dashboards and reports that are intuitive, actionable and minimize misinterpretation.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Economics, Business Analytics or a related technical/business field.

Preferred Education:

  • Master's degree in Data Science, Analytics, Computer Science, Statistics, Business Analytics, or an MBA with strong technical coursework.

Relevant Fields of Study:

  • Computer Science
  • Data Science
  • Statistics
  • Mathematics
  • Economics
  • Business Analytics
  • Information Systems
  • Industrial Engineering

Experience Requirements

Typical Experience Range:

  • 3–7 years of hands-on experience in BI, analytics engineering, data warehousing, or data engineering roles. Candidates early in their career may have 2+ years with strong project experience and dbt/warehouse/BIs.

Preferred:

  • 5+ years designing and operating production analytics solutions, including cloud data warehouses (Snowflake, BigQuery, Redshift), dbt or equivalent transformation frameworks, and one or more BI platforms (Looker, Tableau, Power BI).
  • Demonstrated experience with end-to-end analytics delivery: instrumentation, ETL/ELT, modeling, dashboarding, testing, monitoring and stakeholder enablement.
  • Proven track record of improving data quality, reducing query latency, and driving adoption of self-service analytics across multiple business domains.
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