Manager, Data Engineering
Summary
Leads a team of data engineers, overseeing the design, build, and optimization of scalable data pipelines and architectures that ensure data integrity and support analytics and BI initiatives, while managing projects, automating manual processes, and troubleshooting data quality issues.
The Data Engineering Manager is responsible for leading and overseeing a team of data engineers to design, develop, optimize, and maintain scalable data pipelines and architectures that transform data from source systems into valuable insights. The role requires a strategic thinker with strong technical expertise, leadership skills, and a passion for building efficient, high-performing data systems. This individual will work closely with functional leaders and key stakeholders to drive data automation and analytics initiatives, ensuring data integrity, quality, and alignment with organizational goals.
Key Responsibilities:
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Team Leadership & Development: Lead, mentor, and develop a team of data engineers, fostering a collaborative and high-performing work culture. Provide technical guidance and oversight on complex data engineering projects and initiatives. Conduct technical demos, share research findings, and foster an environment of continuous learning.
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Data Pipeline Management: Create, manage, optimize, and monitor data pipelines to transform and cleanse data from source systems, ensuring data integrity and accessibility for end-users. Oversee the design, build, and maintenance of scalable data architectures that support organizational analytics and business intelligence needs.
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Process Improvement & Automation: Identify, design, and implement process improvements, including automating manual processes and optimizing data delivery. Champion the adoption of best practices in data engineering, leveraging modern technologies to enhance productivity and scalability.
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Project Management: Prioritize, plan, and oversee data engineering projects, maintaining a project catalog and tracking progress toward key milestones and deliverables. Ensure timely delivery of high-quality solutions that meet business requirements.
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Troubleshooting & Optimization: Assist in the analysis, troubleshooting, and enhancement of existing data integration processes. Perform complex data analyses to diagnose and remediate data quality issues, ensuring accurate and reliable data outputs.