Job Description

Job Summary
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 highly experienced, hands-on, and forward-thinking Data Architect to own the architectural vision and roadmap for our data warehousing, reporting, and data delivery automation. In this role, you will define and implement data architecture standards and frameworks, enterprise-scale data models, and guide engineering teams in implementing scalable, high-performance data pipelines and platforms. You will serve as the technical authority on data architecture decisions, ensuring our systems adhere to sound data governance principles, maintain data integrity, and meet the evolving needs of the business. You will collaborate cross-functionally to bridge the gap between raw data and actionable insights, driving alignment between data strategy and organizational goals. This is a hybrid position located in Ann Arbor, Michigan.

Responsibilities*
Design, build, and optimize enterprise data architectures across cloud and on-premises platforms, including data warehouses, operational data stores, pipelines, schemas, APIs, and integration patterns.
Own the architecture and evolution of the SQL Server Operational Data Store, consolidating survey project data from 12+ source systems for near-real-time reporting and field operations, including infrastructure migration, version road mapping, and extension into a historical data warehouse.
Identify authoritative sources of truth across heterogeneous operational systems and design integrated relational models that support reporting and downstream applications while preserving clarity around system ownership and data authority.
Lead hands-on development of data solutions, including ETL/ELT workflows, Python-based ingestion pipelines, REST/GraphQL integrations, file-based imports, operational database extracts, validation logic, business rules, and performance-tuned SQL.
Maintain and extend metadata-driven data services, including curated views, stored procedures, data dictionaries, attribute schemas, dynamic query definitions, UI configuration, and API-layer structures that enable downstream tools without code releases.
Own and evolve SQLXtend, an internal Python data integration framework, including ongoing development, documentation, adoption, and use across team pipelines.
Define and enforce data governance practices, including data standards, metadata management, lineage, data quality controls, security, privacy, compliance, and reliable use of enterprise data assets.
Lead database administration and reliability efforts in partnership with a DBA, including monitoring, backups, replication, performance tuning, indexing, query optimization, capacity planning, security, high availability, and SQL Server upgrades.
Supervise and mentor technical staff, including DBAs, developers, SQL coders, report writers, modelers, analysts, and data engineers by setting technical direction, managing workloads, reviewing deliverables, conducting performance reviews, and supporting career development.
Drive technical strategy and delivery, evaluating new tools and technologies, identifying gaps and redundancies, planning project work, managing risks, collaborating with survey methodologists and project managers, shipping MVPs, gathering feedback, and supporting reporting platforms such as Power BI Report Server and SSRS.

Required Qualifications*
Bachelor's or Master's degree in Computer Science, Information Science, Statistics, Engineering, or a related field, or equivalent professional experience.
10+ years of experience as a Data Architect designing and implementing enterprise-scale data architecture solutions, including data warehousing, integration, reporting, archiving, and near-real-time data consolidation platforms.
Deep expertise in conceptual, logical, and physical data modeling for relational systems, with working knowledge of dimensional modeling and its appropriate use in analytics and reporting environments.
Expert SQL Server skills, including T-SQL, stored procedures, views, indexing, query plan analysis, performance tuning, transactional replication, system-versioned temporal tables, and database security.
Strong proficiency in designing, building, and maintaining robust ETL/ELT pipelines using tools and frameworks such as Apache Spark, dbt, Informatica, Talend, SQL Server-based tooling, or equivalent technologies.
Strong Python skills for data integration, automation, and pipeline development, including consuming REST and GraphQL APIs, handling diverse tabular file formats, and building reusable, testable data processing code.
Experience architecting full-stack web applications backed by SQL Server, particularly internal tools that manage configuration, metadata, workflow, or operational data for other systems.
Proven experience directly supervising, mentoring, and developing technical or engineering staff, including conducting performance reviews, setting team and individual goals, managing workloads, fostering a collaborative team culture, and building high-performing teams.
Demonstrated ability to set technical direction, establish engineering, data security and data architecture best practices, and guide teams toward scalable, maintainable, and secure solutions.
Strong ability to translate complex technical concepts into clear, actionable guidance for both technical and non-technical stakeholders, including survey methodologists, researchers, project managers, business leaders, and engineering teams.
Desired Qualifications*
Experience with DataOps practices in production, including CI/CD, automated testing, observability, alerting, and proactive pipeline monitoring.
Advanced user of Python, SQL Server, version control, and AI-assisted tools such as Claude or GitHub Copilot for coding, testing, documentation, and productivity.
Designed metadata-driven applications and data platforms, including data dictionaries, configurable schemas, dynamic queries, and metadata-controlled validation or transformation.