S
Microsoft Data and AI Engineer
Strategic Staffing Solutions
2 hours ago
Full-time
On-site
Minneapolis, Minnesota, United States
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.
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.