Data Engineering Manager, Core Experience & Incentives

Overview

Instacarts Core Data Engineering (Core DE) and CoreX/Incentives Data Engineering teams sit at the center of our data ecosystem, powering insights and product experiences across the marketplace. We’re looking for an Engineering Manager to lead these two high-impact teams as we scale a centralized data engineering model across the company.

In this role, you will partner closely with Data Science & Analytics, Machine Learning, Product, and Software Engineering to deliver reliable, well-modeled, and trustworthy data that fuels everything from experimentation and incentives to marketplace operations. You’ll help define the operating model for central data engineering at Instacart, roll up your sleeves to drive execution, and build a culture of excellence, ownership, and transparency.

You will manage 7 data engineers across two teams with a mandate to grow, set clear roadmaps and standards, and guide the teams through ambiguity in a fast-paced environment. This is a chance to create durable impact on how Instacart builds, governs, and scales data.

This role is remote-friendly across the United States, with an option to work from San Francisco, CA.

About the Job

• 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.

About You

Minimum Qualifications

• 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.

Preferred Qualifications

• Experience in e-commerce, marketplace, or consumer-scale technology environments.


• Domain experience with incentives, promotions, pricing, growth, or experimentation data products.


• Track record building central/platform data engineering capabilities and defining clear intake and engagement models with product teams.


• Experience with data governance, lineage, and quality tooling (e.g., dbt, Great Expectations, Monte Carlo, DataHub, Amundsen, or similar).


• Demonstrated ability to optimize data systems for cost, performance, and reliability at scale.


• Experience leading distributed or remote teams and establishing effective communication and execution rhythms.


• Familiarity with ML feature engineering and partnering with ML teams (e.g., feature stores, real-time features).


• Advanced degree in a relevant field.