Manager, Data Engineering

About the role

Lead, mentor, and develop a team of 4–10 Data Engineers, fostering accountability, collaboration, innovation, and continuous learning.
Drive end-to-end delivery of high-quality, scalable data solutions across multiple clients, industries, and concurrent projects.
Own the design, development, automation, and maintenance of large-scale enterprise ETL and data integration pipelines supporting a global client base.
Lead Agile team operations including goal setting, sprint planning, capacity management, work allocation, and retrospectives.
Provide ongoing coaching, performance management, and career development support to team members.
Serve as a technical leader and subject matter expert in data architecture, engineering standards, and solution design.
Partner with analytics teams, product managers, and business stakeholders to translate requirements into scalable data solutions.

Responsibilities

• Manage a team of Data Engineers responsible for designing, building, and maintaining scalable data pipelines and enterprise data solutions.
• Own the design, development, automation, and maintenance of large-scale enterprise ETL and data integration pipelines supporting a global client base.
• Lead Agile team operations including goal setting, sprint planning, capacity management, work allocation, and retrospectives.
• Provide ongoing coaching, performance management, and career development support to team members.
• Serve as a technical leader and subject matter expert in data architecture, engineering standards, and solution design.
• Partner with analytics teams, product managers, and business stakeholders to translate requirements into scalable data solutions.
• Leverage SQL and modern database technologies to optimize performance, streamline processing, and manage large-scale datasets efficiently.
• Define and enforce engineering best practices including version control, code reviews, testing frameworks, monitoring, and data quality controls.
• Identify and drive automation and process improvement initiatives that enhance efficiency, scalability, and reliability of data delivery.
• Build and maintain strong relationships with internal and external stakeholders across global teams and client organizations.
• Ensure compliance with Mastercard policies, security standards, and applicable regulatory and regulatory requirements.

Requirements

Proven experience in Data Engineering or a related field, with strong expertise in data architecture, data modeling, database design, and scalable data solutions.
Prior people management experience is preferred. Candidates with strong leadership experience in a team lead or senior individual contributor capacity may also be considered.
Strong track record of delivering complex initiatives while balancing competing priorities and stakeholder needs.
Advanced SQL skills, including performance tuning, query optimization, and large-scale data processing.
Deep hands-on experience with Microsoft SQL Server and relational database technologies.
Experience designing, implementing, and maintaining ETL pipelines and data integration frameworks.
Experience with Databricks, Spark, or modern cloud-based data platforms is a plus.
Interest in emerging engineering productivity tools and AI-assisted development technologies (e.g., GitHub Copilot), with a willingness to explore and adopt new capabilities.
Strong analytical and problem-solving skills, with the ability to think strategically and drive innovation.
Experience managing projects, people, and delivery processes while ensuring quality, accuracy, and operational excellence.
Exceptional communication and stakeholder management skills, with the ability to convey complex technical concepts to both technical and non-technical audiences.

Qualifications

Bachelor's degree in Engineering, Computer Science, Mathematics, Finance, Business, or a related quantitative discipline; equivalent practical experience will also be considered.

Skills

SQL, Microsoft SQL Server, relational database technologies, ETL pipelines, data integration frameworks, Databricks, Spark, cloud-based data platforms, emerging engineering productivity tools, AI-assisted development technologies, Agile methodologies, project management, stakeholder management, communication, data architecture, data modeling, data engineering, data science, data quality controls, compliance, security standards, regulatory requirements.

Benefits

Hybrid work schedule based in Arlington, VA, with an expectation of three days per week onsite.

Pay

Not specified

Schedule

Hybrid work schedule based in Arlington, VA, with an expectation of three days per week onsite.

Benefits

Not specified