Top Data Engineer Jobs in Ann Arbor, MI

The Senior Data Engineer will maintain ChowNow's data platform, enhance data pipelines, work with data scientists, and improve data quality and accessibility, contributing to executive decision-making and business growth.

As a Staff Data Engineer, lead the technical roadmap for data infrastructure, manage complex data architectures, and mentor engineers while driving the organization's data platform innovations.

The Senior Data Engineer at Unchained will design and implement data pipelines, integrate APIs, ensure data governance, and mentor team members while focusing on accuracy and security.

Lead the evolution of SimplePractice's data stack, building scalable systems for analytics, reporting, and product insights in a fast-paced environment.

The role involves writing and deploying scripts for data collection, transforming data sets, diagnosing bugs, and collaborating with other engineers using agile principles.

The Data Engineer will design, develop, and optimize large scale data pipelines and ensure effective data delivery for cross-functional teams.

The Data Engineer at Netflix builds systems for efficient data processing and modeling, collaborating with various teams to empower analytical insights and product features.

Interns will support Databricks ETL framework, improve documentation, validate Metapedia, and engage in AI/ML discovery. Requires good communication and data engineering knowledge.

The Data Engineer II will design and optimize data pipelines, mentor junior engineers, and improve deployment processes to support business data needs.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Develop and productionize a next-gen Dispatch System, build predictive models, automate processes, and lead a small team in optimizing operations.

The Data Engineer will design and maintain ETL pipelines and data storage solutions, collaborating with cross-functional teams to ensure efficient data processing and delivery.

The Lead, Data Engineer leads the data engineering team, designing scalable data solutions, mentoring staff, and ensuring data quality while collaborating with stakeholders to meet business goals.

As a Data Engineer, you will build and optimize data pipelines, manage ETL workflows, and ensure efficient data access and quality within the infrastructure.

The Data Architect designs data flows, defines standards, ensures data quality, and collaborates on data strategies while integrating advanced technologies.

The Data Engineer will build ETL pipelines and data infrastructure, ensure data quality, and implement modern data warehousing for analytics improvements, while collaborating with stakeholders across teams.

This role involves building and maintaining data pipelines, developing data models, and ensuring data discoverability while enabling scalable analytics on blockchain data.

The Senior AI Data Engineer will design and maintain datasets for AI models in cybersecurity, ensuring data quality and relevance while collaborating with teams.

As a Data Engineer, you will design and implement a data platform, maintain data pipelines, work with Airflow and BigQuery, and ensure data compliance while collaborating with teams.

As a Data Engineer, you'll design and maintain data platforms, develop data pipelines, collaborate with teams, and ensure data quality for diverse stakeholders.

The Data Engineer will manage data ingestion in Salesforce, standardize lead data, ensure data quality, and optimize integration workflows.

As a Data Engineer at Thatch, you'll develop data infrastructure, build ELT pipelines, and collaborate across teams to provide analytics insights.

The Data Engineer will build scalable privacy-centric data frameworks, engineer data pipelines, and ensure compliance with privacy regulations at Netflix.

Design and maintain high-quality data pipelines, develop DBT models, collaborate with analytics and data science teams, and optimize data operations for scalability.