Business Intelligence Engineer

Position Summary:

The Business Intelligence Engineer is responsible for designing, developing, and maintaining scalable analytical data solutions that enable enterprise reporting, analytics, self-service capabilities, and data-driven decision-making. This role has a strong engineering focus, with primary responsibility for dimensional data modeling, semantic modeling, advanced SQL development, data transformation, programming, data integration, and performance optimization. The BI Engineer transforms complex source data and business requirements into reliable, reusable, and well-governed analytical data products.

The BI Engineer designs and develops fact and dimension models, star schemas, analytical data marts, semantic layers, standardized business metrics, and reusable transformation logic that support multiple analytical and reporting use cases. The role requires strong hands-on experience with SQL, programming or scripting languages such as Python, and modern data warehouse, transformation, and BI technologies.

The BI Engineer works closely with data architects, data engineers, analysts, and business stakeholders to understand source systems, translate business requirements into technical solutions, and ensure data accuracy, performance, scalability, and usability. The role applies modern software engineering practices including version control, code reviews, automated testing, documentation, and CI/CD.

Dashboard and visualization development remains part of the role, but the primary emphasis is on building the data, modeling, transformation, and semantic foundations that power enterprise analytics.

Major Duties and Responsibilities:

Data Modeling & Semantic Engineering

Design, develop, and maintain dimensional data models, star schemas, analytical data marts, aggregates, and semantic layers. Define fact table grain, dimensions, relationships, hierarchies, measures, and standardized business metrics to create scalable and reusable analytical data products. Apply dimensional modeling and data warehousing best practices to ensure models support enterprise analytics and self-service use cases.

SQL Development & Data Transformation

Develop complex, production-quality SQL and reusable transformation logic to implement business rules and analytical requirements. Build and maintain transformations using dbt or comparable frameworks, optimize query performance, and develop reconciliation and data-quality controls to ensure accuracy and reliability. Troubleshoot complex data issues across transformation and consumption layers.

Programming & Software Engineering

Develop scripts, utilities, and automation using Python or similar programming languages to support data processing, validation, testing, and operational activities. Apply software engineering practices including Git-based version control, peer code reviews, reusable components, automated testing, CI/CD, documentation, and controlled deployments.

Data Integration & Source-System Analysis

Perform detailed source-data analysis, profiling, mapping, and validation to understand data structures, relationships, business rules, and data-quality issues. Translate source-system data into scalable analytical models and collaborate with data engineering teams across ingestion, transformation, modeling, and consumption layers. Maintain appropriate source-to-target mapping, lineage, and traceability.

BI & Analytics Enablement

Develop and maintain governed semantic models, analytical datasets, and standardized business metrics that support enterprise reporting and self-service analytics. Build and enhance dashboards and reports using platforms such as Power BI, Tableau, Looker, MicroStrategy, or comparable technologies while promoting reuse of common models, datasets, and business logic rather than report-specific solutions.

Performance, Governance & Business Partnership

Ensure analytical solutions meet enterprise standards for performance, security, governance, metadata, lineage, documentation, and data quality. Troubleshoot production issues, perform root-cause analysis, and implement sustainable solutions. Partner with business stakeholders, data architects, engineers, and analysts to translate business requirements into scalable technical solutions. Mentor team members and contribute to BI engineering, modeling, and development standards.

Education and Experience:

• Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or a related technical field, or equivalent practical experience.
• 6-8 years of experience designing and delivering scalable, secure, and high-performing business intelligence, analytical data, or data engineering solutions.
• 5+ years of hands-on advanced SQL development, including complex transformations, analytical queries, business-rule implementation, data validation, troubleshooting, and performance optimization.
• 5+ years of experience with dimensional and analytical data modeling, including fact and dimension modeling, star schemas, data marts, aggregates, semantic models, and standardized business metrics.

Knowledge and Skills:

• Hands-on programming or scripting experience using Python or similar languages, along with experience using modern data transformation frameworks such as dbt or comparable technologies for data processing, automation, testing, and analytical engineering.
• Strong experience with modern data warehousing and ETL/ELT concepts, source-system analysis, data integration, Git-based version control, automated testing, CI/CD practices, and enterprise BI technologies such as Power BI, Tableau, Looker, MicroStrategy, or similar platforms.
• Experience supporting production data and analytics environments, including troubleshooting, root-cause analysis, data reconciliation, performance optimization, and sustainable issue resolution.
• Experience mentoring BI developers, engineers, or analysts and promoting best practices in data modeling, SQL development, coding, testing, governance, and analytics engineering.
• Knowledge and Skills
• Advanced proficiency in SQL, including complex joins, CTEs, window functions, aggregations, transformations, analytical queries, and query optimization.
• Strong knowledge of dimensional modeling, including fact table grain, fact and dimension design, conformed dimensions, slowly changing dimensions, hierarchies, aggregates, and star schemas.
• Strong understanding of semantic modeling, governed business metrics, measures, dimensions, and reusable analytical datasets.
• Working proficiency with Python or another programming/scripting language for data processing, automation, testing, validation, and analytical engineering.
• Strong understanding of modern data warehousing, ETL/ELT, data integration, and data transformation principles.
• Experience with modern transformation frameworks such as dbt, including modular transformations, testing, documentation, and version-controlled development.
• Experience with Git-based development practices, peer code reviews, automated testing, CI/CD, and controlled software deployment practices.
• Strong ability to analyze complex source systems, understand data relationships and business rules, identify data-quality issues, and translate source structures into scalable analytical models.
• Ability to troubleshoot data discrepancies by tracing data across source, ingestion, transformation, modeling, semantic, and reporting layers.
• Strong understanding of data quality, metadata management, data lineage, governance, security, and access-control principles.
• Experience with enterprise BI and semantic platforms such as Power BI, Tableau, Looker, MicroStrategy, or comparable technologies.
• Ability to design reusable enterprise data solutions rather than developing isolated, report-specific logic.
• Strong analytical, problem-solving, troubleshooting, and root-cause analysis capabilities.
• Proven ability to translate complex business requirements into technical data models, transformation logic, semantic definitions, and scalable analytical solutions.
• Strong written and verbal communication skills with the ability to collaborate effectively with business stakeholders, data engineers, architects, analysts, and other technical teams.