Senior Data Analytics Engineer

Build the pipelines, semantic layer, and serving infrastructure behind CSS intelligence and detection products at Microsoft. This is a platform-focused role with end-to-end ownership of governed, certified measures, reliability, and data quality, so AI decision systems and executive experiences can trust the numbers they rely on.

Location: United States (onsite)

Compensation: USD 106,400 - 222,600 per yearly

Experience: 2+ years

What you will do

• Design and operate data pipelines that power CSS intelligence, covering both organizational batch processing and near real-time streams where detection latency matters.
• Ingest case and agent telemetry, integrate with a unified data platform and Finance systems, and build production scoring pipelines that run data science models continuously (not limited to notebook workflows).
• Own pipeline reliability through monitoring and alerting, including handling schema drift and late-arriving data so failures surface early to the engineers responsible.
• Build and maintain the semantic layer between raw data and consuming experiences, including dimensional models, certified measures with documented definitions and clear ownership, consistent hierarchies, and security context that governs access.
• Ensure the semantic layer supports more than reporting: the detection engine uses it for baselines and thresholds, and executive and natural-language experiences query it via a semantic API with a traceable query path.
• Embed data quality and observability directly into pipelines with freshness stamps, reconciliation tests against sources of record, contract checks between producers and consumers, and end-to-end lineage for traceability.
• Apply engineering discipline to analytics using source control, deployment pipelines, environment separation, and infrastructure as code. Manage cost and performance via partitioning, incremental refresh, and query optimization.
• Partner with data scientists and applied AI engineers to productionize models, preparing data for AI consumption through grounding, retrieval, and feature-serving patterns agents depend on.
• Design for handoff so schemas, APIs, deployment approaches, and documentation are understandable to teams that did not build the system.

What you bring

• Education and experience: Master’s degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics, or related field AND 2+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis; OR Bachelor’s degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics, or related field AND 4+ years experience in the same areas.
• Hands-on experience with Microsoft Fabric, Azure Synapse, Azure Data Factory, Databricks, or Power BI semantic models in a production environment.
• Experience building data platforms that serve AI or machine learning workloads, including feature serving, retrieval and grounding data, or model scoring at scale.
• Experience implementing data governance in practice, including certified metric definitions, tiered metric ownership, and a promotion process for executive reporting readiness.
• Experience supporting executive-facing analytics where accuracy, freshness, and traceability are required.

Core technologies

SQL, Python, Spark, PySpark, Microsoft Fabric, Azure Data Factory, Azure Synapse, Databricks, DAX, Power BI, CI/CD, Infrastructure as code, API, Natural language, Azure

Additional or preferred qualifications

• Master’s degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics, or related field AND 6+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis; OR Bachelor’s degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics, or related field AND 8+ years experience in the same areas.