Data Engineering Manager, Core Experience & Incentives
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InstacartE-commerce
United States - RemoteFull-TimeManager
Salary183000 - 232000 USD per year
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Job Details
- Experience
- 8+ years of experience in data engineering, 2+ years of experience directly managing data engineering teams
- 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 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.
Responsibilities
- Lead two high-impact teams: Core Data Engineering (Core DE) and CoreX/Incentives Data Engineering.
- Define the operating model for central data engineering at Instacart, drive execution, and build a culture of excellence, ownership, and transparency.
- Manage 7 data engineers across two teams with a mandate to grow, set clear roadmaps and standards, and guide through ambiguity.
- 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 and migrating pipelines from product teams.
- 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.
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