Data Engineering Manager, Core Experience & Incentives
Remote-friendly across the United States, with an option to work from San Francisco, CA.Full-TimeManager
Salary183000 - 232000 USD per year
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Job Details
- Experience
- 8+ years
- Required Skills
- PythonSQLKafkaSnowflakeAirflowSparkScalaData modelingBigQueryRedshiftDatabricks
Requirements
- 8+ years of experience in data engineering building and operating production-grade data pipelines and platforms.
- 2+ years of experience directly managing data engineering teams with full people leadership responsibilities (hiring, performance, and career development).
- Proficiency in SQL.
- Proficiency in Python or Scala.
- Hands-on experience with distributed processing and streaming technologies (e.g., Spark, Kafka, or Flink).
- Experience with modern cloud data warehouses and lakehouse architectures (e.g., BigQuery, Snowflake, Databricks, or Redshift).
- Experience orchestrating pipelines with tools such as Airflow, Dagster, or similar.
- Strong background in data modeling (dimensional and normalized).
- Strong background in data quality frameworks and automated testing.
- Proven success partnering cross-functionally with Product, Data Science, and Software Engineering to deliver end-to-end data solutions.
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience.
Responsibilities
- Own the vision, strategy, and roadmap for Core DE and CoreX/Incentives DE to deliver high-quality batch and streaming pipelines, trusted datasets, and scalable data models.
- Lead, coach, and develop a team of 7 data engineers, creating growth opportunities, establishing clear goals and accountability, and hiring to scale.
- Define and enforce engineering excellence standards for data modeling, testing, data quality, documentation, observability, SLAs, and cost/performance optimization.
- Partner with DSA, ML, Product, and SWE to establish clear data contracts and deliver well-documented, versioned, and discoverable datasets.
- Drive the centralization of data engineering by creating and iterating on intake and engagement models, migrating pipelines from product teams where appropriate, and measuring impact through clear OKRs.
- Ensure strong governance and reliability through incident response, root cause analysis, prevention plans, and adherence to privacy, security, and compliance standards.
- Communicate status, risks, and tradeoffs with clarity and candor to stakeholders and leadership, fostering alignment and predictable delivery.
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