Top Data Engineer Jobs in Detroit, MI

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

Senior Data Engineer on PwC's Managed Data, Analytics & Insights team to design, build and manage advanced data ecosystems. Responsibilities include designing data solutions and scalable pipelines, solving complex problems, mentoring junior staff, maintaining high delivery standards, and building client relationships while aligning solutions to business context.

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.

Design, build, and maintain Databricks-based ETL/ELT pipelines and medallion-layer data products. Integrate enterprise applications (including DataStage and ServiceNow), implement data quality, governance, and operational support, and collaborate with cross-functional teams to deliver scalable, production-ready data assets for analytics, reporting, and AI.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

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.

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.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

Lead and deliver Databricks-based data engineering solutions on cloud platforms. Design, build, and optimize large-scale data pipelines and ETL; mentor engineers; collaborate with product, data, and design stakeholders; implement process improvements and monitoring; support analytics and data science initiatives. Role requires frequent client travel and consulting delivery.

Design, build, and optimize end-to-end data pipelines, ETL processes, and data models to support purchasing and finance analytics. Collaborate with data scientists and stakeholders to deploy predictive models, ensure data quality and governance, and deliver dashboards and analytics for operational insight and forecasting.

Build and maintain cloud infrastructure, CI/CD pipelines, and data pipelines on AWS. Implement IaC (Terraform, AWS CDK), container orchestration (Docker, Kubernetes/OpenShift), DevSecOps practices, monitoring, and data processing using Python, SQL, and Databricks to improve reliability, performance, and observability.

The Data Engineer will manage project execution related to cloud data platforms, using data engineering skills and project management to deliver analytics solutions. The role requires collaboration with business partners and focuses on data products in finance and cost engineering.

The Data Engineer designs and maintains data pipelines for advanced analytics in supply chain, ensuring data quality and reliability while collaborating with cross-functional teams.

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.

Develop, validate, and maintain Power BI reports and dashboards using DAX and Power Query. Extract, clean, and analyze data with SQL, Excel, Python and VBA to support supplier quality metrics (PPAP, CRV, Run at Rate, QPR). Gather requirements, enable drill-down reporting, schedule refreshes, deploy reports to production, and act as SME for supplier quality systems (SPPS, SQMS, SCMS). Hybrid role requiring in-office presence three days weekly.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.

Design, build, and optimize data pipelines for massive datasets. Collaborate with stakeholders, implement CI/CD processes, and ensure data architecture scalability.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.