Description

• Design, build, test, deploy, and support scalable data ingestion, transformation, and integration
solutions using Microsoft SQL Server and Microsoft Fabric.
• Develop reusable ETL and ELT processes that move data from authorized enterprise systems 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 that support Power BI, Excel,
human analysis, and approved AI-assisted analysis

Design enterprise data models using repository-driven standards and Medallion methodology to deliver
governed, high-quality, analytics-ready data assets.
• Integrate structured, semi-structured, and unstructured data from databases, Excel, CSV files,
email-delivered files, APIs, exported reports, and authorized web-based sources.
• Create controlled, repeatable processes for nonstandard data sources with validation, reconciliation,
traceability, and error handling.
• Profile data, identify quality issues, determine root causes, and implement remediation or monitoring
controls.
• Build curated, analysis-ready datasets and semantic models for Power BI, Excel, analysts, and approved
AI tools.
• Use approved generative AI tools to assist with data analysis, SQL and code development,
documentation, testing, troubleshooting, and prompt-based workflows.
• Validate AI-generated SQL, code, calculations, summaries, and analytical conclusions before use.
• Translate business and analytical needs into clear data requirements, technical designs, and usable
data products.
• Follow applicable data governance, privacy, information security, risk, and compliance requirements.

Skills Priority
1 Microsoft SQL Server and advanced SQL data engineering
2 Microsoft Fabric and modern data engineering patterns
3 Enterprise data modeling: repository-driven standards and Medallion methodology to create governed, high-quality, analytics-ready Fabric data assets and reusable models for hypothesis testing and analysis
4 ETL/ELT, data integration, data quality, metadata, and governed data product development
5 AI-assisted development and prompt engineering for data analysis
6 Advanced Microsoft Excel
7 Power BI
8 Azure data services
9 Power Apps / Power Automate
10 Databricks

Required Qualifications
• 7+ years of progressively responsible experience in data engineering, database engineering, analytics engineering, data integration, or a related discipline.
• Advanced hands-on Microsoft SQL Server and complex SQL development experience.
• Experience designing and delivering reusable ETL or ELT pipelines.
• Hands-on Microsoft Fabric experience or strong experience with a comparable modern cloud data platform.
• Strong knowledge of relational, dimensional, semantic, and analytical data modeling.
• Experience designing enterprise data models using repository-driven standards and Medallion methodology to create governed, high-quality, analytics-ready data assets.
• Experience integrating enterprise data with nonstandard sources such as Excel, files, APIs, email-delivered data, exported reports, or authorized website data.
• Demonstrated data profiling, validation, reconciliation, and data quality problem-solving experience.
• Advanced Excel skills, including Power Query, data models, PivotTables, advanced formulas, and external data connections.
• Experience preparing data for Power BI, Excel, human analysts, or AI-assisted analytical workflows.
• Working knowledge of source control, code review, testing, release management, and production support.
• Strong communication skills and the ability to explain technical designs, assumptions, limitations, and findings to nontechnical stakeholders.
• Ability to work independently and manage ambiguity in a complex, regulated enterprise environment.

AI Experience
Candidates should have practical, work-related experience with one or more AI-assisted engineering or
productivity tools, such as Microsoft 365 Copilot, GitHub Copilot, Claude Code, Microsoft Cowork, Devin,
Visual Studio Code with approved AI extensions, or other enterprise-approved LLM tools.
• Developing and refining prompts for data discovery, analysis, SQL generation, testing, documentation,
and interpretation of results.
• Providing schema context, definitions, examples, constraints, and expected output formats within
prompts.
• Validating AI-generated SQL, calculations, code, summaries, and analytical conclusions.
• Recognizing hallucinations, unsupported conclusions, incorrect assumptions, and data leakage risk.
• Creating repeatable prompt templates or AI-assisted workflows that improve analyst productivity.

Preferred Qualifications
Third-Party Recruiting Vendor Brief Page 4
• Microsoft Fabric Lakehouse, Warehouse, Data Factory, Dataflows Gen2, notebooks, or semantic model
experience.
• Azure SQL, Azure Data Lake Storage, Azure Data Factory, or related Azure data services.
• Python, PySpark, PowerShell, or another data transformation and automation language.
• Power BI experience, including semantic models, DAX, Power Query, performance optimization, and
executive reporting.
• Power Apps or Power Automate experience.
• Databricks, Delta Lake, Spark, medallion architecture, or comparable lakehouse experience.
• Experience supporting both traditional business intelligence and AI-based analysis.
• Experience with APIs, JSON, XML, HTML parsing, browser automation, or approved web data extraction.
• Experience working with confidential or sensitive data in a regulated environment.
• GitHub, Azure DevOps, ServiceNow, CI/CD, or automated testing experience

Key Skills

Education

Bachelor's degree