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%)
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- 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.
