Top Data Engineering Manager Jobs

Lead the Enterprise Platform Data Engineering team, overseeing data platform strategy, technical leadership, and team development to enhance data governance and quality across the organization.

The Engineering Manager will lead the Online Data Systems team, focusing on hyperscale database technology and engineering team development, emphasizing reliability and performance.

The role involves leading developer relations for AI platforms, guiding partners on product integration, and fostering collaboration for ecosystem growth.

Oversee, guide, and mentor teams in building unified data access and event-driven architectures, ensuring high reliability and performance standards while managing engineering talent and fostering a collaborative culture.

The Engineering Manager for Data Products will lead a team of engineers in building data analysis products, ensuring high-quality delivery, and fostering collaboration in a remote work environment.

The Engineering Manager will lead teams in developing and managing Blackbaud's Data & AI platform, focusing on scalability, security, and cost-efficiency.

Lead the Data & Cloud Platform for the AI organization, building cloud runtime, unified data access layers, agent memory/context systems, and data ingestion/transformation pipelines. Split time between hands-on engineering and managing a small team, set platform patterns, ensure privacy-aware data handling, and partner with product and governance teams to scale maintainable, cost-effective infrastructure for AI workloads.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

The Engineering Manager will oversee a team managing cloud data engineering projects, client engagement, and technical leadership, ensuring successful delivery and mentorship.

The Senior Manager of Data Engineering will lead a team, manage data platforms, drive modernization, oversee budgets, and ensure data solutions are scalable and reliable.

The role involves leading data engineering and analytics teams, evolving the Helios data platform, and delivering actionable insights for strategic decision-making.

The Senior Engineering Manager leads the Data Platform team, overseeing data engineers, managing data pipelines, and driving Root's data strategy across business functions.

The Enterprise Data Engineering & Platform Manager will lead data modernization initiatives, support AI/ML, manage data pipelines, and ensure platform reliability within a healthcare context.

Manage and grow a data engineering team, overseeing the design and maintenance of data platforms using Snowflake, dbt, and Azure. Drive best practices in data management and ensure reliable data movement and quality.

Lead a team of data engineers in designing and maintaining scalable data infrastructure and pipelines, ensuring data integrity and decision-making support.

Lead design, build, and management of large-scale data structures, pipelines, and ETL workflows. Develop databases, data marts, and tools for real-time and offline analytics. Collaborate with Data Science to operationalize models, ensure data quality and accessibility, troubleshoot systems, and recommend tooling and optimization approaches to meet business needs.

Design, build, and manage large-scale data structures, ETL pipelines, and data marts; optimize Hadoop/HDFS processes; write scalable code (Python/Java); collaborate with Data Science to integrate models; ensure data quality, testing, and system troubleshooting.

Lead data engineering projects to support analytics and product development: translate business needs into analytic questions, perform ETL and data modeling, ensure data quality in the warehouse, build dashboards (Tableau/PowerBI), integrate multiple data sources, mentor junior staff, and manage CI/CD, containerization, and cloud-based deployments.

Manage data engineering projects and teams to design, build, and maintain large-scale ETL pipelines, data marts, and data models. Integrate data sources, ensure data quality, collaborate with Data Science to productionize models, and resolve technical issues. Coordinate strategy across engineering, product, and platform teams and supervise junior engineers.

Lead teams building and operating the core data platform, ingestion pipelines, datalake adoption, Data APIs, permissions, and customer-facing data access to enable reliable, scalable client data delivery and AI-enabled workflows; partner cross-functionally and develop engineering talent while reducing operational burden through automation.

Lead and scale AI/ML engineering teams to deliver production-grade systems. Own end-to-end ML lifecycle (data pipelines, model development, deployment, monitoring, optimization), drive AI strategy, partner cross-functionally, improve engineering execution, and mentor engineers while ensuring scalable, reliable, and cost-efficient AI platforms.

Lead design and delivery of metadata-driven ETL pipelines and cloud-native data solutions. Build PySpark/Informatica IICS frameworks, implement Data Vault 2.0 models on Snowflake/AWS, improve data quality and operating model, integrate analytics teams, and support actuarial data use in insurance projects.

Lead development and evolution of a scalable data platform, partner with technical and business leaders, define technical vision and governance, drive transformational change, build and manage a high-performing data engineering team, and deliver data capabilities for BI, analytics, and ML.

Lead and grow an engineering squad to design, build, and operate the customer data platform. Own architecture, integrations, data governance, delivery practices, hands-on technical leadership, and cross-functional collaboration to enable scalable, secure, real-time customer data products.

The Manager of Data Engineering will lead modernization efforts, optimize the Snowflake environment, manage a hybrid team, and drive data strategy and execution.