Top Data Engineer Jobs in Detroit, MI

The Data Migration Engineer migrates customer data into SaaS products, ensuring data quality and business continuity while collaborating with teams and managing customer requirements.

The Data Engineer will build and maintain data platforms, deploy pipelines, monitor transformations, and collaborate with Data Scientists and DevOps.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

Lead design, build, and maintain scalable data pipelines and infrastructure using Azure Databricks, ADF, and SQL Server. Develop OLTP and dimensional models, integrate Salesforce and SaaS sources, implement batch and real-time processing, enforce data governance and CI/CD, troubleshoot performance, mentor team members, and drive improvements to reduce technical debt and improve cost and performance.

The Lead Cloud Data Engineer will design and optimize ETL pipelines, manage data quality, and collaborate with teams to meet data needs. Responsibilities include monitoring pipelines and mentoring junior engineers, while leveraging AWS and other cloud technologies.

The Data Engineer I will build and maintain data pipelines, contribute to platform code, and integrate data sources while growing alongside senior engineers in a production-focused role.

Support a Department of State data engineering program: review data policies and governance, define requirements, build and maintain Palantir-based data pipelines, clean and integrate datasets, detect and remediate data fidelity and pipeline issues, produce analyses and visualizations, and coordinate with non-technical users and stakeholders.

The Principal Data Engineer will manage the analytics data architecture, engage cross-functionally, and create reliable data systems using Snowflake and Python on AWS.

Lead design and hands-on delivery of a next-generation data platform and pipelines (batch, ELT, near-real-time) powering BI, experimentation, and ML. Own platform architecture, data modeling, governance, observability, and mentor engineers while modernizing legacy systems and driving AI-first data engineering practices across cross-functional teams.

Build and support high-performance data solutions, design and operate ETL pipelines and data warehouses, monitor production analytics tools, troubleshoot system issues, collaborate with product teams, and support TechOps for analytics infrastructure.

Design, build, and maintain scalable batch and real-time data pipelines and platform components. Produce curated datasets, ensure data quality, observability, and lineage, and collaborate cross-functionally to deliver data products that support analytics, experimentation, and ML.

Build and maintain ingestion pipelines, Python services, and dbt models on AWS and Snowflake. Own operational on-call responsibilities, migrate pipelines to Airflow, contribute to CI/CD, collaborate with BI/Analytics, and mentor junior teammates.

Lead end-to-end data engineering on GCP: design scalable data platforms and architectures (lake/lakehouse/warehouse), build and optimize BigQuery/Spark pipelines, ensure data quality/security, enable ML workflows with Data Science, mentor engineers, and own delivery and performance.

Design, build, and optimize data pipelines and storage solutions. Ensure data accuracy and performance, provide technical support, and collaborate with data scientists and stakeholders to resolve data issues and meet requirements.

Design, build, and optimize scalable AWS-based data pipelines, data lakes, and warehouses. Migrate legacy systems, enforce data quality/security/governance, implement ETL/ELT and big-data solutions, adopt CI/CD and IaC, collaborate with stakeholders, and provide technical leadership and troubleshooting.

Design, build, and maintain scalable ETL/ELT data pipelines and BigQuery data models. Implement Airflow workflows, CI/CD for deployments, ensure data quality and performance, and collaborate with data scientists, analysts, and business teams to deliver reliable analytics-ready data.

Lead architecture and delivery of a cloud-native, petabyte-scale data platform: define technical roadmap, mentor engineers, build ETL/ELT pipelines, enable real-time streaming, implement Iceberg table format, optimize Snowflake/Redshift/Athena compute, and integrate observability with Splunk.

Design, build, and maintain scalable data pipelines on AWS using EMR, Spark, and PySpark. Optimize EMR clusters and Spark jobs, implement ETL/ELT processes, ensure data quality, automate ingestion, and collaborate with data scientists and analysts. Document infrastructure, participate in code reviews, and monitor performance and costs.

Design, build, and maintain operational and analytical data platform capabilities using Java Spring Batch, Python, AWS, Oracle, and Snowflake. Deliver end-to-end development, production rollout, and support; improve financial product data quality, modeling, and SQL performance; collaborate with business partners and squads.

Design, build, and maintain ETL/ELT data pipelines and orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, and document processes while collaborating with cross-functional teams and supporting large-scale distributed data systems.

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Responsible for building and operating scalable lakehouse data pipelines, optimizing data flows, and delivering analytics-ready data products using Azure Databricks technologies.

Lead design, build, and operate real-time, Flink-based streaming data platforms. Develop low-latency, stateful pipelines, enforce event schemas/contracts, ensure fault tolerance and observability, mentor engineers, and partner with architecture and compliance teams to deliver resilient streaming solutions for banking use cases.