Top Data Engineer Jobs in Detroit, MI

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver data warehousing and governance solutions, mentor junior staff, and build client relationships while maintaining high standards and business-context awareness.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Lead and manage delivery of modern data ecosystems and managed data analytics programs. Oversee project planning, budgeting, quality, and client accounts. Mentor teams, drive data strategy, convert insights to business opportunities, and ensure standards and risk management across large transformations.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

Develop and maintain data pipelines and ETL processes, optimize SQL and data architectures, build BI reports and dashboards (Oracle BI, QlikView, Spotfire), analyze data to produce strategic insights, support client engagements in regulated environments, and apply continuous process improvements to ensure data quality and integrity.

Develop and maintain data pipelines and BI solutions for smart factory initiatives. Design data models, document data warehouses, build KPI dashboards, support ad-hoc analysis, and collaborate with stakeholders to enable data-driven decisions.

Build and improve end-to-end simulation data workflows and automation for vehicle dynamics: generate, store, process, validate, and analyze simulation datasets; implement CI/CD pipelines, Databricks/SQL-based data management, and automation; partner with simulation engineers to standardize metadata, drive reuse, and train teams toward data-driven simulation practices.

Design, develop, and maintain scalable data platforms, ETL/ELT pipelines, and tabular/dimensional data models across on-premises and cloud environments. Build and schedule data processing jobs, ensure data quality, performance tuning, and troubleshooting. Lead code reviews, participate in on-call production support, mentor engineers, and drive best practices and technical debt remediation.

Build reusable, analytics-ready telematics data products on Azure Databricks. Design canonical datasets, standardize metrics and business logic, optimize performance and cost, enable self-service analytics, and support AI/ML use cases for enterprise telemetry and connected-vehicle data.

Build, scale, and operate an enterprise telemetry data platform that ingests, processes, and publishes vehicle telemetry at global scale. Develop batch and streaming pipelines using Databricks and Spark, implement observability and data quality controls, optimize distributed workloads for performance and cost, and enable reliable production data products through CI/CD and Infrastructure as Code. Provide technical leadership and partner with analytics, AI, and engineering teams.

The Data Engineer will develop data pipelines, support data automation, collaborate with Agile teams, and ensure data reliability and performance. The role requires strong ETL skills and experience with relational databases, big data analysis, and CI/CD practices.

Design, build, optimize, and maintain large-scale data pipelines and analytics solutions. Analyze complex datasets, improve pipeline performance and cost, collaborate with cross-functional teams, and support the Insights product suite using cloud, Databricks, and modern data tooling.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

Set up test, preproduction and developer environments on physical servers; act as SQL Server DBA including security auditing; develop Big Data solutions and programs; perform user acceptance and integration testing. Requires experience with DTS/SSIS/SSRS, T-SQL, C#, PowerShell, Hive/Hadoop, and Microsoft SQL Server 2000 certification.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Design, build, and maintain enterprise analytics solutions and semantic data models using Microsoft Fabric, Databricks, SQL, Python, and Power BI. Develop reporting datasets, dashboards, and self-service analytics; ensure data quality, performance, CI/CD, and governance; translate business requirements into scalable solutions; provide technical leadership, mentor engineers, and collaborate with architects and stakeholders to align analytics with business objectives.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.

Design, build, deploy, and support scalable batch and streaming data pipelines and cloud infrastructure on GCP. Develop analytical data products and data warehouse solutions, implement data governance and quality monitoring, automate infrastructure with Terraform and CI/CD, and collaborate with stakeholders to deliver secure, cost-effective finance data solutions.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

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.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

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

Build and maintain production data pipelines and models using SQLMesh, BigQuery, and Python. Improve observability, reliability, and performance; support finance and revenue pipelines; participate in on-call rotation and own features end-to-end.