Outstanding long-term contract opportunity! A well-known Financial Services Company is looking for a Principal Microsoft Data & AI Engineer in Minneapolis, MN, Irving, TX, or Charlotte NC. (Hybrid). Work with the brightest minds at one of the largest financial institutions in the world. This is a long-term contract opportunity that includes a competitive benefit package! Our client has been around for over 150 years and is continuously innovating in today's digital age. If you want to work for a company that is not only a household name, but also truly cares about satisfying customers' financial needs and helping people succeed financially, apply today.

Contract Duration:

18 Months Required Skills & Experience 7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work or consulting experience, training, military experience, education. 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. Desired Skills & Experience Business Intelligence & Reporting Advanced MS Excel Artificial Intelligence (AI) Azure Databricks Data Modeling Microsoft Fabric Components Power bi fabric SQL 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. What You Will Be Doing Consult as an expert to develop or influence initiatives and resources for highly complex business and technical needs across Engineering. Consult on the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas, delivering solutions that are long-term, large-scale and require vision, creativity, innovation, and advanced analytical and inductive thinking. Provide expertise to client senior leadership on innovative Engineering business solutions. Strategically engage with client personnel. 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.