Top Data Engineer Jobs in Ann Arbor, MI

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

Own and scale Provable's data platform on GCP: build and optimize ingestion pipelines from blockchain indexers, design privacy-aware data models, manage Postgres and BigQuery, develop high-performance data-serving APIs, ensure data quality and observability, and use IaC (Terraform, Helm) and Kubernetes to deploy and cost-optimize environments.

The Sr. Data Engineer designs ETL solutions and data architecture, collaborates on technical solutions, and addresses data management functions.

Design, build, and maintain scalable ETL/ELT data pipelines and data platform architecture. Build programmatic pipelines (primarily Python), optimize analytical data models (SQL), integrate sources into Snowflake, use orchestration/transformation tools (dbt, Airflow, Dagster), improve data quality/observability, and partner with product, engineering, and analytics to deliver reliable data for decision-making.

Design, build, and maintain scalable data pipelines and integrations across AWS, Azure, and BigQuery. Normalize, aggregate, and ensure data quality for large web and transactional datasets, leveraging APIs, ETL/orchestration tools, and big-data technologies to support analytics and BI in an e-commerce environment.

Build and maintain scalable data pipelines for healthcare claims; implement de-identification and PII-reduction; ensure data quality and observability; integrate data stores and APIs; draft technical designs and documentation; gather product feedback and iterate quickly.

Lead performance investigations across the data product stack to diagnose and fix slow queries and UX issues. Design data architecture changes, build benchmarks, measure latency/throughput/cost (ClickHouse Cloud), prototype solutions, instrument monitoring, and partner with product and engineering to balance responsiveness, scalability, and cost.

Extract, transform, and load data from clients' legacy databases into CentralSquare systems. Write and tune SQL/ETL processes, present and validate converted data with clients, assist testing and go-live conversions, document processes, estimate project scope, and seek guidance from leadership when needed.

Designs and implements big-data data management solutions (Cloudera/Hortonworks/Databricks). Builds and automates reporting and analytics using SQL and Databricks, optimizes queries, manages ETL, integrates multiple data sources, enforces data governance, and supports payment integrity reporting requirements.

Design, build, and migrate large-scale batch data pipelines into Databricks. Develop and optimize production-grade Python/Spark/SQL solutions, support parallel legacy and cloud systems during migration, lead technical design discussions, use AI tools to improve workflows, ensure pipeline reliability through testing/monitoring/tuning, and participate in a rotating on-call schedule.

Own design and implementation of a greenfield analytical data platform: ingesting PostgreSQL and Kafka streams, establishing governance, data lineage, quality, monitoring, and compliance-ready pipelines. Evaluate AWS-native tooling, Snowflake, and Databricks; produce RFCs and documentation; mentor junior engineers and ensure analytical systems remain downstream of transactional sources.

The Senior Data Engineer will design and maintain scalable cloud-native data pipelines, develop APIs, implement advanced analytics, manage AI workflows, and enhance data operations collaboratively.

Design, architect, and implement end-to-end data ingestion, storage, modeling, and provisioning solutions on the GE Predix platform. Lead cross-functional teams, set data architecture standards, develop OLTP/OLAP models, perform POCs, and enable analytics at scale while enforcing data management policies.

Lead design and scaling of backend data infrastructure for Beacon's Datastore: build event-driven pipelines, data models, and APIs; draft RFCs; integrate services; improve operational robustness, documentation, and tooling to support clinical and scientific workflows.

The Senior Data Engineer will design and implement data processing pipelines using Java and Spark, optimize performance, and mentor junior engineers while ensuring compliance with data governance standards.

Founding data engineer to design and build Payabli's data platform: architect lakehouse/warehouse, build batch and streaming pipelines, model canonical datasets, ensure data quality/observability, enforce access/masking/lineage for regulated financial data, and enable analytics/ML feature pipelines while establishing team standards and CI/CD.

The Senior Data Engineer will maintain and improve data infrastructure, design ETL processes, and create high-performance pipelines for analytics and AI initiatives using SQL and Python in cloud environments.

Build and maintain scalable ETL/data pipelines and internal frameworks to support ML and product teams. Drive architecture, observability, data quality, and engineering best practices across AI/ML workflows and prompt-engineering pipelines.

Lead design, build, and operate scalable Databricks/Delta Lake data pipelines and Kimball dimensional models using dbt. Manage governance with Unity Catalog, optimize performance and cost, operationalize ML with MLflow, and coordinate/quality-check offshore vendor engineering. Mentor engineers, uphold standards, and deliver trusted data products that power analytics, reporting, and MLOps workflows.

Design, build, and maintain dbt data models and scalable ELT pipelines; optimize performance-aware SQL on Snowflake; ensure observability, testing, and CI/CD; partner with product and engineering teams to meet analytic needs, mentor engineers, document business logic, and troubleshoot ingestion, transformation, and warehouse issues.

Design, build, and maintain scalable data pipelines, data models, ETL/ELT, and BI solutions. Collaborate with data scientists and stakeholders to improve data quality, accessibility, and PHI security. Develop and document workflows, optimize processing/storage, support data warehouse and visualization, and adopt AI tools to drive data-informed outcomes.

Design, build, deploy, and maintain large-scale analytics solutions and ETL pipelines. Implement real-time streaming and batch processing, optimize SQL and data flows, ensure data quality, contribute to dev-ops and QA, cross-train team members, and participate in community and best-practice activities.

Design, build, deploy and maintain large-scale analytics and ETL solutions. Manage data ingestion, real-time streaming and batch processing across data stores, tune SQL and data flows, ensure quality, performance and maintainability, participate in QA/devops, cross-train team members, and contribute to community and best practices.

Design, build, and optimize scalable cloud data pipelines and infrastructure. Maintain and refactor SQL and ETL processes, automate data workflows, ensure data quality/security, and troubleshoot complex issues. Provide technical leadership and mentor junior engineers while collaborating with stakeholders to translate requirements.

Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.