Data Analytics Engineer

Our data warehouse sits at the center of how the business runs: it is used by stakeholders ranging from executives to finance to operations to clinic level providers and staff. We have data architecture/engineering to keep the pipelines flowing and report builders turning models into insight. What we need now is the person who owns the layer in between. As our Analytics Engineer, you will own the SQL side of the house: the curated schema of reporting views, business logic, and semantic model foundations that transform raw warehouse tables into clean, consistent, trustworthy data products. When a metric shows up on an executive dashboard or inside one of our applications, your work is the reason everyone trusts the number. This is a hands-on builder role for someone who thinks in data models, writes SQL fluently and defensively, and takes pride in being the person who makes messy source data make sense. You'll also lean on AI tools as part of how you build using coding assistants to move faster on SQL and documentation without ever outsourcing your judgment about what's correct. You will be the connective tissue between the engineers who land the data and the analysts who present it, and you will have real ownership of how that middle layer is designed.

As our Analytics Engineer, you will own the SQL side of the house: the curated schema of reporting views, business logic, and semantic model foundations that transform raw warehouse tables into clean, consistent, trustworthy data products. When a metric shows up on an executive dashboard or inside one of our applications, your work is the reason everyone trusts the number. This is a hands-on builder role for someone who thinks in data models, writes SQL fluently and defensively, and takes pride in being the person who makes messy source data make sense. You'll also lean on AI tools as part of how you build using coding assistants to move faster on SQL and documentation without ever outsourcing your judgment about what's correct. You will be the connective tissue between the engineers who land the data and the analysts who present it, and you will have real ownership of how that middle layer is designed. WHAT YOU'LL OWN

WHAT YOU'LL OWN - The reporting schema: Design, build, and maintain the reporting view layer of our SQL Server / Azure SQL data warehouse: the governed, documented views that serve as the single contract between raw data and everything downstream. - Business logic in SQL: Translate business definitions into durable SQL logic so metrics are defined once, in one place, and reused everywhere.

- The reporting schema: Design, build, and maintain the reporting view layer of our SQL Server / Azure SQL data warehouse: the governed, documented views that serve as the single contract between raw data and everything downstream. - Business logic in SQL: Translate business definitions into durable SQL logic so metrics are defined once, in one place, and reused everywhere. - Semantic model foundations: Own the structure of our Analysis Services / Power BI semantic models: tables, relationships, and core measures, so report builders can work from a clean, performant model rather than reinventing logic per report.

- Data quality and reconciliation: Build validation checks and reconciliation queries that catch problems before stakeholders do. Investigate discrepancies, trace them to root cause, and fix them at the right layer. - Performance of the reporting layer: Write efficient T-SQL, tune the queries and views that power reporting workloads, and know when a view should become an indexed view, a materialized table, or a redesign. - Documentation and lineage: Maintain clear documentation of the reporting schema, metric definitions, and data lineage so the warehouse is understandable, not tribal knowledge.

- Documentation and lineage: Maintain clear documentation of the reporting schema, metric definitions, and data lineage so the warehouse is understandable, not tribal knowledge. - AI-assisted development: Use AI coding assistants (e.g., GitHub Copilot, ChatGPT, Claude) to accelerate T-SQL development, documentation, and code review while retaining full ownership and accountability for the correctness of what goes live into production. - Cross-functional partnership: Partner with data engineering on upstream schema changes, with BI developers on model needs, and with business stakeholders to turn ambiguous asks into concrete, correct data definitions.

WHAT YOU BRING - 3+ years working hands-on with SQL Server or Azure SQL in an analytics, data warehousing, or reporting context - Expert-level T-SQL: complex joins, window functions, CTEs, and the judgment to know when a query is correct versus merely returning rows

- 3+ years working hands-on with SQL Server or Azure SQL in an analytics, data warehousing, or reporting context - Expert-level T-SQL: complex joins, window functions, CTEs, and the judgment to know when a query is correct versus merely returning rows - Real experience with dimensional modeling and reporting-layer design (star schemas, conformed dimensions, slowly changing logic), and opinions about when to break the rules

- Working knowledge of the Microsoft BI stack: Analysis Services (tabular), Power BI semantic models, and enough DAX to design models that measure-writers can build on - Comfort using AI-assisted development tools (e.g., GitHub Copilot, ChatGPT, Claude) to draft, review, and debug complex SQL with the judgment to validate AI-generated logic before it goes into production, rather than accepting output at face value - A track record of turning vague business questions into precise data definitions, and of defending data quality when it would be easier to ship the number

- Power Query / M experience, or experience supporting Excel-based analytical consumers of the warehouse - Familiarity with modern analytics engineering tooling and practices (dbt or similar), even if our stack is SQL-native - Experience evaluating or adopting AI-assisted BI features (e.g., Power BI Copilot, natural language query tools) in a reporting environment

- Exposure to version-controlled model development (TMDL, Tabular Editor, Git-based workflows) - Power Query / M experience, or experience supporting Excel-based analytical consumers of the warehouse - Familiarity with modern analytics engineering tooling and practices (dbt or similar), even if our stack is SQL-native

- Business logic in SQL: Translate business definitions into durable SQL logic so metrics are defined once, in one place, and reused everywhere. - Semantic model foundations: Own the structure of our Analysis Services / Power BI semantic models: tables, relationships, and core measures, so report builders can work from a clean, performant model rather than reinventing logic per report. - Data quality and reconciliation: Build validation checks and reconciliation queries that catch problems before stakeholders do. Investigate discrepancies, trace them to root cause, and fix them at the right layer.

- Real experience with dimensional modeling and reporting-layer design (star schemas, conformed dimensions, slowly changing logic), and opinions about when to break the rules - Working knowledge of the Microsoft BI stack: Analysis Services (tabular), Power BI semantic models, and enough DAX to design models that measure-writers can build on - Comfort using AI-assisted development tools (e.g., GitHub Copilot, ChatGPT, Claude) to draft, review, and debug complex SQL with the judgment to validate AI-generated logic before it goes into production, rather than accepting output at face value

- Familiarity with modern analytics engineering tooling and practices (dbt or similar), even if our stack is SQL-native - Experience evaluating or adopting AI-assisted BI features (e.g., Power BI Copilot, natural language query tools) in a reporting environment WHY THIS ROLE