Description
Short Description
This position manages the delivery and operations of assigned data platforms, pipelines, integrations, and related capabilities. The role is accountable for reliable, secure, governed, and cost-effective operation of the team's services, enabling trusted data products and timely business decisions. The manager develops a high-performing team and delivers prioritized outcomes within established enterprise strategy, architecture, and engineering standards.
Job Responsibility
• Manages team intake, delivery plans, and ongoing support for assigned data platforms, pipelines, integrations, and data products; communicates scope, risks, dependencies, and tradeoffs to stakeholders and escalates material cross-team issues to the Director. (20%)
• Applies established data-engineering standards and architecture practices to team delivery; contributes implementation input to technical roadmaps, design reviews, and platform lifecycle decisions. (15%)
• Ensures reliable operation of the team's production data services through agreed service targets, observability, incident response, production support, change management, and recovery practices; monitors cost and capacity for assigned services and escalates material risks. (15%)
• Hires, coaches, develops, and performance-manages 4–8 direct reports; supports workforce planning and identifies staffing, skill, or vendor needs for Director review. (15%)
• Applies approved data-product quality practices, including schema and data contracts, testing, reconciliation, documentation, monitoring, and discoverability; drives resolution of quality issues within the team's services. (10%)
• Drives team-level improvement in engineering automation, tooling, and delivery practices, including responsible use of approved AI-assisted tools in accordance with security, code-review, company-data protection, and engineering standards. (10%)
Job Qualifications
• Strong knowledge of data engineering, data platforms, data pipelines, databases, integrations, and modern cloud data services.
• Strong hands-on proficiency with Python, SQL, ETL/ELT, orchestration, data modeling, production operations, and data product quality practices.
• Working knowledge of Google Cloud Platform and BigQuery; familiarity with Cloud Composer/Airflow, Dataflow, Pub/Sub, dbt, and Terraform is preferred.
• Ability to apply engineering standards, participate in architecture and design reviews, and make sound team-level technical and delivery decisions.
• Working knowledge of data privacy, security, governance, access controls, retention, lineage, metadata, and compliance requirements, with the ability to implement applicable controls in team delivery.
• Demonstrated people leadership in hiring, coaching, performance management, and team development.
• Strong stakeholder management, prioritization, communication, analytical, and problem-solving skills, including the ability to communicate delivery tradeoffs.
• Ability to use approved AI-assisted engineering tools responsibly to improve productivity, testing, documentation, troubleshooting, and data quality work while safeguarding company data.
Education/Experience
• Ability to use approved AI-assisted engineering tools responsibly to improve productivity, testing, documentation, troubleshooting, and data-quality work while safeguarding company data
• A bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field, and 3-5 years of progressively responsible experience in data engineering, software engineering, analytics engineering, or related disciplines, including 1–2 years of technical leadership, team-lead, or people-management experience.
• Demonstrated experience delivering and supporting production data pipelines, integrations, or data products. Hands-on experience with Python, SQL, ETL/ELT, orchestration, data modeling, cloud data services, and production operations.
• Experience with Google Cloud Platform and BigQuery is strongly preferred; experience with Cloud Composer/Airflow, Dataflow, Pub/Sub, dbt, and infrastructure-as-code is a plus. Experience implementing data privacy, security, governance, quality, lineage, and access-control requirements within engineering delivery is required. Experience responsibly using approved AI-assisted engineering tools to improve development, testing, documentation, and operational productivity is preferred.
• An equivalent combination of education and/or experience may be considered.
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