About the RoleAs an Analytics Engineer, you will build and maintain analytics assets that turn business logic into reliable, reusable data products. Your work will include dimensional models, trusted datasets, governed metrics, and semantic layers that reporting, analytics, and business teams can confidently use.
This is not a traditional reporting role, and it is not primarily a pipeline engineering role. You will work in the layer where business logic becomes reusable analytics infrastructure: models, metrics, semantic layers, and trusted datasets.
You will partner with business stakeholders, BI developers, analysts, and data team members to clarify requirements, improve metric consistency, and build assets that are reliable, documented, and maintainable. Success in this role will be measured by adoption, consistency, trust, and maintainability - not output volume.
What You'll Do- Build and maintain dimensional models, reusable datasets, and semantic layers for reporting, analytics, and decision-making.
- Translate business logic into clear, tested, well-documented data structures.
- Support KPI, metric, calculation, and reusable business logic definition with stakeholders and data team members.
- Make sound modeling decisions across grain, relationships, performance, usability, governance, and maintainability for assigned work.
- Partner with business users, analysts, BI developers, and senior data team members to turn business questions into durable analytics solutions.
- Follow and contribute to analytics development standards, including naming conventions, documentation, certification, reusable measures, and change control.
- Improve trust in reporting by reducing duplicated logic, documenting definitions, and making shared metrics easier to understand and maintain.
- Use AI-assisted workflows to improve development speed, documentation, testing, and data quality while applying judgment to protect trust in the data.
What We're Looking ForRequired Qualifications- 3+ years of experience in analytics engineering, BI engineering, data modeling, or a closely related analytics role.
- Strong SQL skills, including experience building, testing, and maintaining analytical transformations.
- Solid understanding of dimensional modeling, including facts, dimensions, grain, relationships, and conformed dimensions.
- Experience building data models, semantic layers, BI models, or trusted datasets used for reporting and analysis.
- Ability to translate business requirements into clear, maintainable data structures.
- Ability to communicate clearly with both technical and non-technical stakeholders.
- Good judgment in balancing speed, quality, performance, usability, governance, and maintainability.
- Experience working in a modern analytics platform such as Microsoft Fabric, Snowflake, Databricks, BigQuery, or similar.
Preferred Qualifications- Hands-on experience with Microsoft Fabric.
- Power BI experience, including DAX, relationships, performance optimization, or semantic model development.
- Experience with Python, data quality testing, version control, CI/CD, or other practices that improve analytics engineering reliability.
- Experience improving reporting trust through clearer metric definitions, documented business logic, or reusable data models.
- Experience using AI-assisted development workflows responsibly.
Who Will Thrive in This RoleYou will thrive here if you enjoy turning messy business logic into trusted, reusable data products. You should be comfortable working through ambiguity, asking good questions, challenging unclear requirements, and balancing practical delivery with long-term maintainability.
You should be motivated by building durable analytics assets that improve how the business operates, not just answering one-off reporting requests.
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