Data Engineering Manager Jobs

About The Position

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Data Science, Information Systems, Business Analytics, or a related field is preferred; an advanced degree or equivalent experience is welcomed.
• Strong understanding of modern analytics engineering, data architecture, data modeling, and data-product principles.
• Advanced knowledge of SQL and strong understanding of cloud data platforms and modern enterprise data ecosystems.
• Strong understanding of dimensional modeling, fact and dimension design, data grain, keys, historical data, slowly changing dimensions, analytical marts, and downstream consumption patterns.
• Knowledge of modern transformation and orchestration approaches, including SQL-based transformation frameworks, Python, and comparable tools to dbt and Apache Airflow.
• Understanding of software-engineering practices for data development, including Git, peer review, automated testing, CI/CD concepts, reusable code, environment management, and controlled deployment.
• Knowledge of data governance, lineage, metadata, data quality, ownership, privacy, and enterprise data-management principles.
• Understanding of business intelligence and analytical consumption patterns, including Power BI and how upstream data architecture affects semantic models, metrics, reporting, and analytics.
• Understanding of how trusted, reusable data products can enable AI, advanced analytics, intelligent decision-making, and emerging data-driven capabilities.
• Advanced knowledge of AI-ready enterprise data architecture and scalable data platform design patterns.
• Advanced hands-on SQL experience, including complex transformations, dimensional modeling, analytical marts, and production-grade reusable datasets.
• Experience with cloud data warehouses.
• Experience using Python for data processing, automation, validation, reconciliation, or related data-engineering use cases.
• Experience translating complex or ambiguous business problems and metric needs into scalable data architecture, models, transformations, and reusable data solutions.
• Significant experience designing analytics-ready data foundations and governed enterprise data products, including ownership, quality, lineage, and documentation.
• Ability to navigate fragmented data environments independently, investigate unfamiliar data domains, and lead scalable solutions amid evolving requirements.
• Experience applying modern analytics engineering practices, including version control, automated testing, code review, documentation, deployment, and monitoring.
• Experience collaborating across Business, Analytics, Data Engineering, Architecture, Data Governance, and Technology teams to deliver enterprise data solutions.
• Demonstrated ability to balance immediate delivery priorities with long-term architecture, reusability, governance, and maintainability.
• Strong communication skills, including the ability to explain technical concepts, architecture decisions, and trade-offs to nontechnical stakeholders.
• Experience mentoring data practitioners and advancing scalable analytics engineering and data-product practices.
• Experience leading implementation of AI-enabled data platform modernization or operational transformation initiatives.
• Experience designing scalable data infrastructure supporting advanced analytics, ML, or GenAI workloads.
• Experience establishing governance and quality standards for AI-consumable enterprise data assets.

Nice To Haves

• Google BigQuery experience is strongly preferred.
• Experience with dbt or comparable SQL-based transformation frameworks and Apache Airflow or similar orchestration technologies is preferred.
• An advanced degree or equivalent experience is welcomed.

Responsibilities

• Lead the design and development of trusted, analytics-ready data foundations that transform complex enterprise and workforce data into reusable, business-ready datasets.
• Discover complex or fragmented workforce data domains, including source systems, business definitions, data grain, keys, history, dependencies, quality considerations, and downstream consumption needs.
• Design scalable data models, transformation layers, and architectures for workforce metrics, reporting, advanced analytics, and AI.
• Establish consistent approaches to business rules, historical data, reusable dimensions, data quality, and lineage.
• Partner with business and technical stakeholders to translate analytical needs into scalable, reusable data products rather than one-off solutions.
• Define data-product architecture, transformations, business logic, quality standards, documentation, and ownership.
• Balance immediate business priorities with long-term architectural scalability, maintainability, and reuse.
• Lead hands-on development of complex data transformations using SQL, BigQuery, and Python, with tools such as dbt, Git, and Apache Airflow or comparable technologies.
• Establish engineering standards for modular development, version control, testing, deployment, orchestration, and monitoring.
• Implement automated data-quality, validation, reconciliation, and lineage capabilities to improve trust and reliability.
• Serve as a technical bridge across Business, Analytics, Data Engineering, Architecture, Governance, and enterprise technology teams.
• Shape analytics engineering and workforce data strategy, identifying opportunities to simplify fragmented data ecosystems and build scalable capabilities.
• Enable reliable data consumption across Power BI, Excel, SQL, Python, and emerging AI solutions.
• Mentor team members and upskill analysts in SQL, data modeling, data-product thinking, reusable transformation patterns, data quality, and modern analytical development practices.
• Promote a data-product mindset centered on business value, reliability, reusability, maintainability, automation, quality, and ownership.

Benefits

• Competitive base salaries
• Bonus incentives
• 6% company match on retirement savings plan
• Free financial coaching and financial well-being support
• Comprehensive medical, dental, vision, life insurance, and disability benefits
• Flexible working model with hybrid, onsite, or virtual arrangements depending on role and business need
• 20+ weeks of paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption, or surrogacy
• Free access to global onsite wellness centers staffed with nurses and doctors, depending on location
• Free and confidential counseling support through the Healthy Minds program
• Career development and training opportunities