Top Senior Data Engineer Jobs in Detroit, MI

Gather client requirements and architect, develop, and deploy cloud data platforms, warehouses, ETL processes, pipelines, APIs, and BI solutions. Perform advanced SQL and data modeling, support deployments and integrations, diagnose technical issues, mentor staff, contribute to innovation and practice development, and assist with project management. The role requires approximately 20% overnight travel and primarily in-person work.

Leads and develops a data engineering team implementing Navigate360 integrations for partner campuses. Oversees integration design, data mapping, validation, reconciliation, quality controls, delivery planning, and scalable implementation processes. Partners with Product, Launch, and Partner Success teams to resolve technical and partner challenges, improve adoption, manage project health, and ensure reliable outcomes. Provides technical mentorship, code and design review, stakeholder communication, and continuous process improvement.

Design, build, and own business-critical data pipelines and infrastructure across warehouses, orchestration, BI, and multiple regions. Develop AWS Glue and Spark jobs, data models, event contracts, observability, and batch or streaming architectures. Collaborate across producer and consumer teams, support Federal workloads, and operate production data platforms in government cloud environments.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Lead the architecture and development of Vercel’s next-generation data platform, supporting batch and real-time integrations, analytics, data warehousing, and AI/ML workloads. Design scalable systems using Kafka, ClickHouse, Tinybird, and Snowflake; establish data governance and security standards; guide architectural decisions and roadmaps; collaborate with engineering, product, security, compliance, and leadership teams; write production code; and mentor engineers.

Designs, builds, and maintains scalable ETL and data processing pipelines supporting analytics, forecasting, risk, and customer insights. Leads complex SQL development, data modeling, governance, quality, lineage, security, and compliance. Partners with engineering, finance, sales, compliance, product, analysts, and data scientists to translate requirements into reliable workflows. Evaluates emerging technologies, leads proof-of-concepts, and develops automated solutions using Python, Spark, Databricks, cloud platforms, orchestration tools, and modern data warehouses.

Leads the design, development, optimization, and migration of modern data platforms in Snowflake. Builds pipelines with dbt, Python, and cloud services; gathers client requirements; creates solution designs and project plans; advises on Snowflake performance, cost, security, and workload management; integrates BI, governance, and AI/ML tools; contributes to accelerators and mentors junior engineers. The role is remote, client-facing, consultative, and focused primarily on technical delivery rather than sales.

Build and operate batch and streaming data pipelines, ETL/ELT workflows, data warehouses, dimensional models, and reusable data services. Improve reliability, performance, quality, and cost efficiency through monitoring, testing, automation, and incident response. Partner with analytics, data science, product, business, platform, and infrastructure teams to deliver production data solutions and roadmap initiatives. Apply AI-assisted tools responsibly to development, testing, documentation, and troubleshooting.

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

Build and operate scalable batch, streaming, and event-driven data pipelines, cloud data platforms, trusted data products, and analytical models. Improve data quality, observability, reliability, performance, and cost efficiency while supporting risk, finance, product, analytics, and AI initiatives. Partner cross-functionally, influence architecture, lead complex technical initiatives, mentor engineers, and establish strong engineering practices including testing, CI/CD, governance, and infrastructure automation.

Lead data engineering strategy and develop enterprise data lake solutions on AWS. Build data pipelines using AWS data integration, engineering, and analytics resources, develop API services with FastAPI or Flask, optimize engineering practices, create long-term data plans, and represent the team in executive meetings.

Build and maintain dbt and semantic data models in Snowflake, develop ingestion and transformation pipelines, define trusted product metrics, and improve data collection. Partner with Product and Engineering to support experimentation and feature analysis. Establish data quality testing, documentation, HIPAA-aware access controls, and compliance practices. Use and validate AI-generated code and Snowflake Cortex outputs while raising technical standards across the team.

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.

Design, build, and operate production data pipelines using Python, Spark SQL, and Airflow. Canonicalize healthcare and customer data into trusted internal models, optimize distributed processing, enforce schemas, and implement quality checks and observability. Support customer onboarding, streaming pipelines, troubleshooting, on-call operations, and platform best practices while collaborating with product, analytics, engineering, data science, and customer teams.

The Senior Data Engineer designs and maintains production data pipelines on GCP using BigQuery, Dataform, Airflow, Dataflow, and Cloud Run. They own data validation, cleansing, troubleshooting, API integrations, and reliable datasets for analytics and AI/ML. The role also involves translating client requirements into data solutions, maintaining dashboards, supporting ad hoc requests, and applying Git, code reviews, and CI/CD practices.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.

Design, build, and operate scalable ETL/ELT pipelines and backend data services using Go. Integrate relational, NoSQL, blockchain, API, web, and social data sources; develop NLP pipelines for text extraction and classification; and support production APIs, dashboards, alerts, and intelligence systems. Collaborate with engineering, data science, and investigations teams on architecture, data quality, lineage, validation, monitoring, and client-facing products.

Design, build, and maintain scalable batch and streaming data pipelines using Python, SQL, Kafka, and GCP. Develop REST and GraphQL data services, CI/CD automation, observability, and data-quality frameworks. Integrate ML and NLP models into production workflows, including continuous retraining and real-time inference. Support structured and unstructured healthcare datasets, distributed systems, microservices, and reliable data infrastructure while collaborating with data science and MLOps teams.

Lead the design and optimization of healthcare data pipelines integrating EHR, payer, HIE, and vendor data. Build AWS-based ETL/ELT infrastructure, reusable integration tooling, data quality monitoring, and ML enablement foundations. Ensure HIPAA-compliant handling of clinical data, including security controls, audit logging, encryption, and de-identification. Provide technical leadership, mentor engineers, influence platform architecture, and lead complex partner integrations involving FHIR, HL7v2, CCDA, and healthcare terminology standards.

Architect, build, and operate scalable data platforms processing billions of daily events through real-time and batch pipelines. Own production reliability, data quality, security, observability, and performance while partnering with engineering, analytics, data science, growth, and business teams. Develop frameworks and datasets, make architectural decisions, evaluate technologies, mentor engineers, and help define the platform roadmap.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.

Design and operate production-grade data pipelines, backend services, APIs, and agentic AI workflows. Build scalable systems that ingest, transform, and operationalize data for customer-facing and internal applications. Translate ambiguous operational workflows into reliable architectures, improve service observability and performance, and own technical decisions across data modeling, system design, security, and compliance.

Own and expand Chicory’s analytical data platform supporting advertising, campaign, financial, product, and publisher reporting. Design BigQuery architecture, build batch and streaming pipelines with Python, SQL, Beam, Dataflow, and Airflow, and develop governed data marts and BI datasets. Establish data quality, observability, reconciliation, access controls, cost management, and engineering practices. Partner with engineering, finance, product, campaign management, sales, and leadership while leading incident recovery and documenting systems.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.