Microsoft Data Privacy Engineer Jobs (NOW HIRING)

Job Description Principal Microsoft Data & AI Engineer Primary Location: Minneapolis, MN - Strongly Preferred Additional Locations: Chandler, AZ | Irving, TX | Charlotte, NC Schedule: Hybrid - 3 days onsite / 2 days remote Experience: 7+ years Position Overview We are seeking a Principal Microsoft Data & AI Engineer to design and deliver scalable, governed data solutions supporting workforce analytics, operational reporting, executive decision-making, and AI-assisted analysis. This is a senior, hands-on engineering position requiring advanced expertise in Microsoft SQL Server, Microsoft Fabric, SQL, data engineering, and enterprise data modeling, along with practical experience using Generative AI tools to accelerate data analysis and solution development. The ideal candidate can take ownership of complex data problems from initial source assessment through ingestion, transformation, data quality, modeling, testing, documentation, and delivery of trusted, analysis-ready data products.

Core Technology Environment Microsoft SQL Server / Advanced SQL Microsoft Fabric Fabric Lakehouse & Warehouse Power BI / Fabric Microsoft Azure Advanced Microsoft Excel ETL / ELT Enterprise Data Modeling Generative AI / AI-Assisted Development Azure Databricks - future platform; access expected Q1 2027 Key Responsibilities Design, build, test, deploy, and support scalable data engineering and integration solutions using Microsoft SQL Server and Microsoft Fabric. Develop reusable ETL/ELT pipelines that move enterprise data into governed analytical data stores and semantic models. Write and optimize advanced SQL, including complex queries, stored procedures, views, transformations, and performance-tuned processing routines.

Design relational, dimensional, semantic, and analytical data models supporting Power BI, Excel, human analysis, and approved AI-assisted analysis. Design enterprise data models using repository-driven standards and Medallion methodology to produce governed, high-quality, analytics-ready data assets. Build reusable analytical models supporting hypothesis testing, business intelligence, and advanced analysis.

Integrate structured, semi-structured, and unstructured information from databases, Excel, CSV files, APIs, exported reports, email-delivered files, and authorized web-based sources. Build controlled and repeatable processes for nonstandard data sources with appropriate validation, reconciliation, traceability, and error handling. Profile data, identify quality issues, perform root-cause analysis, and implement remediation and monitoring controls.

Develop curated datasets and semantic models for Power BI, Excel, analysts, executives, and approved AI tools. Use approved Generative AI tools for SQL/code development, data analysis, documentation, testing, troubleshooting, and prompt-driven workflows. Validate AI-generated SQL, code, calculations, summaries, and analytical conclusions before production use.

Translate business and analytical requirements into technical designs and usable data products. Ensure solutions comply with enterprise data governance, privacy, security, risk, and regulatory requirements. Required Qualifications 7+ years of experience in data engineering, database engineering, analytics engineering, data integration, or a closely related discipline.

Advanced hands-on experience with Microsoft SQL Server and complex SQL development. Strong experience designing and delivering reusable ETL/ELT pipelines. Hands-on experience with Microsoft Fabric or a comparable modern cloud data platform.

Strong knowledge of relational, dimensional, semantic, and analytical data modeling. Experience with Medallion architecture/methodology and governed enterprise data models. Experience with data profiling, validation, reconciliation, metadata, and data quality management.

Experience integrating traditional enterprise data with Excel, files, APIs, exported reports, and other authorized nonstandard data sources. Advanced Microsoft Excel, including Power Query, PivotTables, data models, advanced formulas, and external data connections. Experience preparing data for Power BI, Excel, analytical, and AI-assisted workflows.

Understanding of source control, testing, code review, release management, and production support. Strong communication skills with the ability to explain technical solutions to business and executive stakeholders. Ability to independently manage complex and ambiguous assignments within a regulated enterprise environment.

Generative AI Experience Candidates should have practical professional experience using AI-assisted development or productivity tools such as Microsoft 365 Copilot, GitHub Copilot, Claude Code, Devin, or other enterprise-approved LLM tools. Experience should include: Prompt engineering for data discovery, SQL generation, analysis, testing, and documentation. Providing schemas, definitions, constraints, examples, and expected output formats to AI tools.

Validating AI-generated SQL, code, calculations, and analytical conclusions. Identifying hallucinations, unsupported conclusions, incorrect assumptions, and potential data leakage. Developing repeatable prompt templates or AI-assisted workflows that improve engineering and analyst productivity.