Top Data Engineering Manager Jobs

Lead and manage a team building and operating enterprise data pipelines, warehousing, and integration solutions. Oversee data architecture, ETL, data quality, governance, BI support, vendor selection, and cross-functional collaboration to ensure reliable, scalable, and secure data for business reporting and decision-making.

Manage and enhance sustainability technology solutions (Microsoft Fabric, MSM, ERA), oversee data pipelines, dashboards, integrations, compliance, and cloud infrastructure. Lead change management, maintain documentation and training, troubleshoot data/metric issues, prioritize feature backlog, and mentor potential direct reports supporting sustainability reporting and analytics.

Lead and mentor a team of data engineers to design, implement, and maintain scalable data pipelines, ensuring data quality and compliance with healthcare regulations.

The Data Engineering Manager oversees the data delivery lifecycle by leading a distributed team, ensuring timely ETL solutions, and driving system integration and innovation.

The Senior Manager leads modernization of data engineering capabilities, ensuring reliable and scalable data systems while managing a team of data engineers to deliver impactful data products.

This role involves leading the Data Engineering team, managing large-scale data pipeline delivery, ensuring data quality, and fostering partnerships with other teams for data governance and project management.

The Manager of Data Engineering leads the delivery of client data migrations, mentors a team, and enhances technical delivery methodologies.

The Senior Manager of Data Engineering oversees the design and management of data platforms, mentors a team of engineers, and ensures project delivery meets quality standards. Responsibilities include hands-on technical contributions, team management, and stakeholder collaboration.

Lead and manage the AI Data Engine team, focusing on ML dataset generation and acceleration of data pipelines while collaborating with cross-functional teams.

Lead and manage the AI Data Engine team, driving strategies for ML dataset generation and improving data warehouse efficiency. Mentor engineers and collaborate across teams.

Lead the Data & ML Platform team, overseeing data engineering, real-time systems, and ML enablement, while building reliable, scalable data platforms and improving data architecture.

Lead end-to-end delivery of AI and data platform programs: define roadmaps, scope and sequence work, manage dependencies and risks, and drive integrations across Finance and applications to enable AI readiness and reliable, event-driven data flows.

Lead and scale the data engineering function: design and maintain Azure-based data warehouse and high-performance ETL pipelines, ensure data quality and governance, mentor the team, collaborate with stakeholders, and drive data strategy and innovation.

Lead and manage the engineering team owning Grata's proprietary company data. Oversee large-scale ingestion, entity resolution, search/indexing, pipeline reliability, data quality, and cross-functional delivery while coaching engineers and balancing short-term delivery with long-term technical health.

Lead the data engineering team to modernize data architecture, ensure data quality, and manage the internal data platform for analytics and decision-making.

The Manager Cybersecurity Engineering - Data & AI will lead security initiatives, manage technical projects, and oversee cybersecurity operations related to data and AI technologies, ensuring compliance and security posture for IT systems.

As an Engineering Manager for Transformation & Data Lake at Snowflake, you'll lead efforts in building interoperable data solutions, focusing on metadata, integration platforms, and data pipeline automation, driving innovation across cloud data ecosystems.

Lead and build an end-to-end data testing and quality assurance function, driving data governance adoption, automation and AI-enabled testing, stakeholder alignment, metrics reporting, and remediation across divisions while managing a team in Dallas.

Lead DDRI technical research team, manage engineers and students, establish software development best practices, oversee internal computing and cluster resources, liaise with faculty on research, define technical standards and project evaluation criteria, mentor staff, direct code review and ensure scalable, robust components across cloud, databases, web apps and ML work.

Lead end-to-end delivery of data engineering programs: define data requirements, design integrations, ensure data quality/observability, manage releases, align stakeholders, and operationalize analytics on cloud platforms.

Lead and grow an engineering team building data quality systems, intelligent labeling workflows, and large-scale data mining to surface edge cases. Drive integration of foundation models, set technical direction and roadmaps, partner with research and infrastructure teams, and hire and mentor engineers to scale high-fidelity labels and accelerate ML iteration for autonomy.

Lead and manage data engineering teams to build and maintain high-quality data pipelines, implement automation and performance scripts, integrate LLMs/Agentic AI, collaborate with product and engineering, oversee data modeling, ensure data quality, and support SEC-related data solutions.

Lead and scale the Helios operational data platform and Analytics & Insights function. Own Snowflake-based data platform, data modelling, governance, and self-service APIs. Deliver insight-driven dashboards, define KPIs, drive data-informed decisions, evolve operating models, and embed AI adoption to improve technology operations.

Manage and develop a BI/data engineering team: hire, train, set goals, oversee systems, ensure data accuracy and security, build and maintain dashboards, datasets (QVDs/Power BI), coordinate with IT, and deliver actionable analytics.

Lead the Annuity Data Platform product team to define strategy, deliver first-launch data capabilities, and operationalize scalable, secure data pipelines. Drive roadmap, cross-team delivery, DevSecOps and SRE practices, data governance, and AI enablement while managing vendors, workforce planning, costs, and stakeholder partnerships to support annuity modernization outcomes.