Top Data Engineer Jobs in Detroit, MI

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.

Lead managed data and analytics engagements building modern data ecosystems. Manage teams and client accounts, deliver managed services, develop data strategies, mentor staff, uphold quality, and convert insights into strategic opportunities for clients.

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms. Analyze complex problems, mentor junior staff, maintain data governance and security, and build client relationships to deliver actionable data solutions.

Design, implement, and manage advanced data ecosystems and scalable data pipelines. Analyze complex problems, deliver managed data and analytics solutions, maintain high-quality deliverables, mentor junior team members, and build client relationships while ensuring data governance and analytics best practices.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, collaborate with clients, and support data strategy and analytics.

Lead design and implementation of data architecture, data models, ETL/ELT pipelines, and data warehouses/lakes. Ensure data governance, security, cloud deployments, IaC, and pipeline reliability. Mentor and coach team members, collaborate with stakeholders to translate requirements, troubleshoot systems, and optimize performance, cost, and quality.

Design, build, and maintain data pipelines and ETL processes; optimize SQL and databases; create BI reports and dashboards; apply data architecture and modeling; support client engagements; ensure data quality and process improvement in regulated environments.

Lead design and implementation of data architecture and pipelines, manage large-scale projects, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms (AWS/Azure/GCP). Develop data models, ensure governance and security compliance, analyze complex problems, mentor junior staff, build client relationships, and enhance technical skills to deliver high-quality data solutions.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop and manage data lakes and warehouses, apply data architecture and dimensional modeling, validate data quality, implement security best practices, and collaborate with clients to deliver analytics and data strategies. Mentor peers and manage growing client responsibilities as a Senior Associate.

Lead design and delivery of cloud-based data architecture, data models, ETL/ELT pipelines, data warehouses/lakes, and data security. Manage projects, mentor team members, ensure governance compliance, collaborate with stakeholders, and optimize cloud infrastructure for performance, cost, and scalability.

Lead design and implementation of data architecture and cloud-based data engineering solutions. Manage large projects, translate stakeholder requirements into technical designs, ensure data governance and security, develop data models and pipelines, advise clients strategically, and coach teams to deliver operational excellence.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

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.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Lead design and delivery of data engineering solutions for clients: build and maintain data pipelines and architectures, implement BI and analytics (AWS/Redshift, ETL, DataStage), collaborate with stakeholders, coach teams, manage managed-services SLAs, apply systems thinking to validate outcomes and refine solutions, and drive performance tuning and client-facing engagement.

Lead and deliver data engineering projects: design and maintain ETL/ELT pipelines, cloud analytics and BI solutions, mentor teams, manage client accounts, and drive data-driven strategic insights and process improvements.

Build and maintain Power BI reporting for FP&A: extract and transform data (Bronze/Silver), semantic modeling and dashboards (Gold), ensure accuracy, security, performance, and manage Power BI Service access and role-level security.

Design, build, and maintain scalable end-to-end data ingestion and curation pipelines on GCP. Ensure data quality, governance, security, and cost optimization. Use Python, SQL, dbt/Dataform, Astronomer, and Terraform to automate workflows and infrastructure. Collaborate with architects and stakeholders, document processes, and translate business requirements into production-ready data assets for analytics and AI consumption.

Design, build and maintain data warehousing and ETL solutions (Informatica IICS, Teradata, Oracle PL/SQL, MS SQL). Create CI/CD pipelines, automate testing and monitoring, operate in agile/XP teams, mentor peers, design data pipelines on GCP/BigQuery/Airflow/Terraform/GitHub, and support development, validation and deployment of AI/ML applications.

Build and maintain Databricks data pipelines ingesting Snowflake and data lake sources, implement medallion architecture transformations, ensure pipeline health and monitoring, create curated datasets for analytics and ML, and partner with data scientists and stakeholders.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.