Analytics EngineerAPEIThe Analytics Engineer designs, builds, tests, and maintains governed analytics assets that transform trusted enterprise data into reusable, decision-ready information. This role develops certified semantic models, metrics, dimensions, business logic, and supporting data structures that provide consistent meaning across reports, dashboards, analytical tools, and AI-enabled capabilities.
Working between data engineering and experience delivery, the Analytics Engineer translates approved business and decision requirements into scalable analytical assets while ensuring quality, lineage, documentation, performance, security, and release standards are met. The role collaborates with Product Management, Data Engineering, Governance Operations, analysts, and business subject-matter experts, but does not independently define business priorities, create source-system ingestion, or own the final user experience.
Responsibilities:
- Build, test, document, and maintain governed semantic models, certified metrics, dimensions, calculations, and reusable analytical assets.
- Translate approved business capabilities and decision requirements into consistent analytical logic used across reports, dashboards, workflows, and AI-enabled capabilities.
- Partner with Data Engineering to confirm that source data, transformations, and Gold Information Products support the intended analytical use.
- Collaborate with Product Management, analysts, Governance Operations, and business subject-matter experts to clarify requirements and validate analytical meaning.
- Define and certify measures, thresholds, targets, comparisons, warning conditions, and dimensional relationships.
- Maintain automated quality tests, metadata, lineage, documentation, certification evidence, and release records for assigned assets.
- Optimize analytical models and queries for performance, scalability, reuse, and efficient consumption across supported platforms.
- Support controlled promotion of assets through development, testing, and production environments in accordance with release, security, and compliance standards.
- Monitor production assets, investigate quality or performance issues, assess downstream impacts, and implement approved corrections.
- Contribute to peer reviews, shared engineering standards, naming conventions, reusable patterns, and continuous improvement across the Enterprise Intelligence organization
Requirements:
- Experience developing semantic models, analytical data models, reusable metrics, dimensions, and business calculation logic.
- Strong SQL skills and experience working with cloud data platforms, preferably Snowflake.
- Experience with analytics engineering, data transformation, testing, version control, and deployment practices.
- Working knowledge of dimensional modeling, data quality, metadata, lineage, access controls, and release management.
- Ability to translate business and decision requirements into scalable, governed analytical assets.
- Experience optimizing models and queries for performance, reliability, and reuse.
- Familiarity with business intelligence and analytical consumption tools, preferably Power BI.
- Ability to troubleshoot data, logic, performance, and downstream dependency issues.
- Strong documentation, collaboration, and communication skills.
- Experience working in a regulated or controlled environment is preferred, including SOX, FERPA, or similar compliance requirements.
- Familiarity with dbt, Git, CI/CD, and AI-ready semantic design is preferred.
Education:
- Bachelor's degree in Data Analytics, Information Systems, Computer Science, Engineering, Business Analytics, or a related field
- Relevant certifications in Snowflake, dbt, Power BI, data modeling, analytics engineering, or cloud data platforms preferred.
