Engineering Manager, Data Platform
Leads and contributes to a data platform team building lakehouse infrastructure, ingestion, processing, governance, and access systems. Requires 12+ years of data infrastructure or backend platform experience, strong Spark expertise, and at least 2 years of engineering management or formal technical leadership.
About the job
Responsibilities
Technical contribution
- Write production code and lead architectural design for data-platform pipelines, storage layers, access APIs, and observability infrastructure.
- Own technical decisions involving Spark job structure, Iceberg table management, and streaming architecture on MSK/Kafka.
- Review code and shape engineering practices for distributed systems.
- Drive modernization and tooling choices across Snowflake, dbt, Airflow, Trino, and related technologies.
- Contribute to data governance, including quality frameworks, lineage, access control, GDPR, and SOC 2 compliance.
Team leadership
- Work directly with engineers on design documents, code reviews, and incident response.
- Provide continuous technical and career feedback, remove blockers, and support engineer growth.
- Own the team roadmap and sequence technical work based on sound engineering judgment.
- Lead technical recruiting, including defining evaluation criteria and conducting hiring processes.
- Manage coordination and stakeholder communication so engineers can focus on deep technical work.
Requirements
- 12+ years of software engineering experience in data infrastructure, distributed systems, or backend platform engineering.
- Production experience with Apache Spark, including job tuning and the Catalyst optimizer.
- Familiarity with Iceberg, Kafka or MSK, Airflow or MWAA, Trino or Athena, and AWS cloud services.
- Experience with Snowflake, dbt, and data access APIs or platform tooling.
- 2+ years of engineering management or formal technical leadership experience, including performance reviews, hiring, or leading significant technical programs.
- Ability to combine hands-on software development with people management.
- Strong communication skills for explaining technical trade-offs to product and business stakeholders.
- Experience in a startup or high-growth SaaS environment.
Nice-to-haves
- Exposure to AI/ML data pipelines or real-time analytics.
Compensation and Benefits
- No compensation information provided.
Skills
Spark, Pyspark, Scala, Apache Iceberg, Apache Kafka, Amazon Msk, Apache Airflow, Mwaa, Trino, Amazon Athena, AWS, Snowflake, dbt, S3, Aws Glue
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