Engineering Manager, Data Platform
Leads the Data Platform team in building reliable, scalable infrastructure and developer tooling for data workloads. The role combines hands-on technical leadership, roadmap and architecture ownership, cross-functional collaboration, and hiring and developing engineers.
About the job
Responsibilities
- Lead the Data Platform team in designing, building, and maintaining infrastructure and tooling for analytics, data science, and data engineering workloads.
- Partner with the Principal Engineer to define the technical roadmap and architecture.
- Collaborate with data science, analytics, product, and engineering teams to shape platform features around user needs.
- Ensure reliability, scalability, usability, observability, and governance across data ingestion, transformation, and orchestration systems.
- Establish data engineering and platform development best practices.
- Provide hands-on technical leadership through design discussions, code reviews, and coding contributions.
- Hire, coach, and develop engineers while fostering technical excellence, ownership, and continuous improvement.
- Communicate roadmap, priorities, and ongoing work to stakeholders.
Requirements
- 7+ years of software engineering experience, including 2+ years leading teams or projects.
- Experience building internal platforms or developer tools, ideally in a data-intensive environment.
- Strong understanding of modern data infrastructure, including orchestration, warehousing, streaming, and transformation tools.
- Proficiency with cloud infrastructure, containers, and infrastructure as code.
- Ability to lead cross-functional workstreams and translate user needs into robust platform capabilities.
- Product mindset focused on solving user problems.
- Willingness to remain hands-on and contribute technically while developing a high-performing team.
Technical Environment
- Airflow or Dagster
- BigQuery or Snowflake
- Kafka
- SQLMesh
- AWS or Google Cloud
- Containers
- Infrastructure as code
Skills
Apache Airflow, Dagster, BigQuery, Snowflake, Apache Kafka, Sqlmesh, AWS, GCP, Containers, Infrastructure As Code, Data Ingestion, Data Orchestration, Data Governance, Observability
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