Staff Software Engineer, Data Governance & Foundations

Instacarts Data Infrastructure organization builds and operates the systems that power our company’s data ecosystem, including a modern open data lakehouse on Apache Iceberg, a multi-engine compute platform for stream and analytical workloads, and self-serve tooling that helps Product, Data Science, ML, Ads, Finance, and engineering teams move fast with data.

We’re looking for a Staff Software Engineer, Data Infrastructure to join our Data Governance and Foundations Team. In this role, you’ll serve as a senior technical leader owning the architecture and delivery of our open lakehouse foundation, governance and access patterns, and multi-engine compute strategy—balancing today’s reliability with the next three to five years of scale, maturity, and cost efficiency.

You’ll collaborate closely with engineering leadership and stakeholders across Data Science, ML Platform, Ads Infrastructure, Finance Engineering, Product Engineering, and Security. You’ll operate with a high degree of ownership in a fast-paced environment where architectural decisions have real technical and financial consequences. If you thrive on complex, high-scale challenges and roll-up-your-sleeves execution, this is a chance to shape the backbone of Instacart’s data platform.

Our stack includes technologies such as Apache Iceberg, Apache Flink, Trino, ClickHouse, Apache Kafka, Apache Spark, Snowflake, Databricks, Confluent, Airflow, dbt, Delta Lake, Scala, Python, Postgres, and AWS. You’ll join a focused team of 7 engineers that values pragmatism, clarity, and impact.

• Translate Instacart’s data strategy (e.g., monetization, federated access, real-time) into an actionable multi-year architecture roadmap; align with leadership while evolving the platform for scale, maturity, and cost efficiency.
• Own the open lakehouse foundation: define and deliver unified table formats, storage governance, and a multi-engine compute portfolio (interactive, batch, streaming) that enables portability and prevents lock-in.
• Drive real-time and streaming infrastructure for critical use cases (Ads, Fraud, ML): set deployment patterns, SLAs, and operational practices that balance performance, availability, and spend.
• Pioneer AI-native data infrastructure engineering by applying LLM/AI tools to the platform lifecycle—accelerating development, automation, observability, and cost optimization—and partnering to embed AI-powered capabilities into the platform.
• Elevate engineering excellence: lead architecture reviews, mentor senior/staff engineers, influence hiring, and clearly communicate complex trade-offs to both technical and executive audiences to ensure cross-org alignment.