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 for mission-critical product and analytics use cases.
Lead, coach, and develop a team of 7 data engineers, creating growth opportunities, establishing clear goals and accountability, and hiring to scale as needs evolve.
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 that power experimentation, incentives, and marketplace operations.
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.

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 and at least one programming language used for data engineering (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), 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 against clear SLAs/OKRs.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringdata pipelinesSQLPythonScalaSparkKafkaFlinkdata modelingautomated testing

Soft Skills

leadershipcoachingcommunicationgoal settingaccountabilitycollaborationrisk managementclaritycandoralignment

Certifications

Bachelor’s degree in Computer ScienceBachelor’s degree in EngineeringBachelor’s degree in Mathematicsequivalent practical experience