Top Data Engineer Jobs in Detroit, MI

Design and deploy scalable data infrastructure and models, maintain CDC pipelines, build reporting tools and dashboards, write high-performance SQL, partner with product and engineering to enable enterprise-wide analytics and data-driven decision making.

Responsible for designing, developing, and maintaining scalable data pipelines and infrastructure, implementing data governance, and collaborating with teams to meet data needs.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

As a Data Analytics Engineer I, you'll bridge engineering and analytics, manage data pipelines, collaborate with stakeholders, and utilize modern open-source tech for data transformation and integration in structured finance.

As an Associate Engineer in Data Science, you will leverage data analysis and machine learning to solve complex problems and develop inspiring digital products.

Nagarro seeks an Associate Principal Engineer in Big Data to leverage extensive IT experience for strategic organizational value. The role values self-starters and innovation.

Design, build, and maintain data lakes, data warehouses, and end-to-end data pipelines for a multi-tenant SaaS platform. Lead code and design reviews, write infrastructure-as-code, optimize performance and scalability, participate in data governance, mentor junior engineers, evaluate new technologies, automate tests, collaborate cross-functionally, and join on-call rotation to ensure reliable data delivery.

Design and execute SQL-based ETL processes to migrate legacy public safety data into Mark43. Collaborate with customers, contractors, and internal teams to map legacy schemas, handle edge cases, ensure data fidelity, and improve migration tooling while managing timelines and stakeholder relationships.

The Senior Data Engineer designs and maintains data infrastructure, creating scalable data pipelines and ensuring data quality while collaborating with cross-functional teams.

Design, build, and maintain an internal data platform enabling secure, governed access for engineering and analytics. Partner with architects, implement data pipelines and automations, enforce security and compliance, support platform services, recommend architecture improvements, mentor teams, and drive adoption of scalable, self-service data capabilities.

The Senior Azure Data Platform Engineer manages Azure SQL databases and Data Factory operations, ensuring stability and performance in a complex environment. The role involves troubleshooting, automating processes, and engaging with clients while leading data load ownership initiatives and enhancing operational practices.

The Principal Data Platform Engineer leads the architecture and delivery of data platforms using Databricks, focusing on data pipeline design and client relationships.

Design and maintain scalable data models and pipelines for analytics. Collaborate with teams to ensure reliable data supporting business insights.

Design, implement, and manage scalable multi-cloud data platforms and ELT pipelines (AWS/Azure). Build and optimize Snowflake data models, dbt transformations, and Airflow orchestration. Define data engineering standards, enforce governance and quality, collaborate cross-functionally, and mentor global/offshore teams.

The Senior Data Engineer will design and maintain data pipelines, write production-grade Python and SQL code, and support AI initiatives while collaborating across teams.

The Senior Data Engineer will scale and evolve the data platform, develop ETL/ELT pipelines, establish best practices, and optimize systems for performance while collaborating with various teams.

Implement and maintain ETL processes to ingest and transform hospital data into Meduit platforms. Build data models, ensure data integrity, document and track tasks in Jira, collaborate with cross-functional teams, support revenue-cycle data implementations, and mentor team members on best practices.

The Sr. Data Engineer will build and maintain data pipelines, develop ETL processes, and optimize data infrastructure, collaborating across teams to ensure data reliability and performance.

The Senior Data Engineer will build and maintain ETL/ELT pipelines, optimize SQL queries, and collaborate on machine learning workflows, driving data engineering best practices.

Lead architecture and implementation of scalable data systems and pipelines from ingestion to delivery. Own ambiguous, high-impact integrations, improve data quality and reliability, adopt AI-augmented engineering, mentor engineers, and collaborate with Product, ML, and customer teams to ship production solutions.

Design, optimize, and scale Snowflake-based data architecture and pipelines. Implement data observability, operationalize MLOps, build semantic enterprise models, and deploy AI-assisted agents and automation for production analytics and commercial data products. Share cross-functional ownership for pipeline resilience, monitoring, and model lifecycle management.

Design, build, and operate production-grade data ingestion pipelines and dbt transformations into a Databricks lakehouse. Establish data quality, monitoring, and observability. Partner with Product and Engineering to enable metrics, dashboards, and longitudinal patient analytics supporting clinical and operational decision-making.

The Staff Data Engineer will enhance the data ecosystem at WeightWatchers by developing data pipelines, collaborating cross-functionally, and ensuring data quality while leading projects and technical initiatives.

The Sr. Data Engineer - Databricks designs and maintains scalable data solutions using Apache Spark and Databricks, ensuring data quality and performance for analytics and reporting.

Design, implement, and support enterprise data integrations connecting JDE, Workday, Salesforce and other platforms. Lead SQL-driven ETL/ELT pipelines, develop reusable integration frameworks, ensure data integrity and audit readiness, perform regression and production troubleshooting, and provide architectural oversight and mentorship across multiple concurrent projects.