Principal Data Architect

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Working at the University of Michigan Institute for Social Research (ISR) means being a part of a team committed to discovery and understanding. It means working for the world's largest academic social science survey and research organization, and doing work that really matters to social science in the public interest. The Survey Research Center (SRC) is a collaborative community that conducts cutting-edge research that focus on critical societal issues such as aging, health disparities, and social networks.

This position is in the Technical Services Group (TSG) within the Survey Research Operations (SRO ). SRO is the operations branch of the Survey Research Center ( SRC ) at the ISR.

At TSG, we are driven by a passion for harnessing technology to build innovative solutions that power the full data collection lifecycle. We are looking for a hands-on Principal Data Architect to lead the team that builds and operates the data pipelines, operational data store, reporting system and data delivery automation behind SRO's survey operations. You will lead and develop a team of data engineers, database administrators, and report developers, set the engineering standards for how data is ingested, transformed, validated, and delivered, and stay close enough to the code to guide design and resolve complex production problems.

This position is posted as aPrincipal Data Architect (Data Architect Staff Specialist), and may be filled at the Data Architect Lead level (see Underfill Statement below).

• Lead, supervise, and develop a team of data engineers, database administrators, SQL developers, and report developers, including hiring, goal setting, workload management, performance reviews, and career development.
• Remain hands‑on as the team's senior technical contributor: lead design reviews, review pull requests, write and performance‑tune T‑SQL and Python, and step in on complex production issues.
• Own production ETL/ELT pipelines that ingest data from 12+ source systems via REST/GraphQL APIs, file‑based imports, and operational database extracts, with defined expectations for latency, completeness, and correctness.
• Own the SQL Server Operational Data Store and its evolution, including near‑real‑time consolidation for reporting and field operations, infrastructure migration, version road mapping, and extension into a historical data warehouse.
• Establish data engineering standards and Data Ops practices, including source control, code review, CI/CD for database and pipeline code, automated testing, orchestration, monitoring, alerting, and data quality checks.
• Own and evolve SQLXtend, the team's internal Python data integration framework, and the metadata‑driven data services (curated views, stored procedures, data dictionaries, dynamic query definitions, and API‑layer structures) that let downstream tools change behavior without code releases.
• Ensure the security and reliability of data systems that handle sensitive research data, partnering with database administrators on high availability, backups, replication, performance tuning, and SQL Server upgrades in line with University and SRO security standards.
• Drive technical strategy and delivery with project managers, survey methodologists, and operations stakeholders: prioritize and plan work, manage risk,…