Senior Data Engineer

We're reimagining enterprise Identity and Access Management from the ground up. Founded by serial entrepreneurs Shai Morag and Tal Marom and backed by a $60M seed round from Greylock, Accel, and CRV, we're building the next generation of identity security. Identity is the gateway to everything- who's let in, who's kept out, and it's now the number one attack vector in the enterprise. The tools built to govern it were designed for a slower world of human users and static environments. That world is gone. Identities are exploding across human, machine, and AI agents faster than legacy systems can track, and security teams are left patching together five disconnected tools that still can't answer "who has access to what, right now." We think the answer isn't another point solution- it's a new foundation. Oak is the AI-native Identity Operating System: an AI connector framework that reaches any application, a live identity graph built from raw evidence, and a team of AI agents that governs the full lifecycle of every identity in one platform. We're building Oak's AI-native core from day one-engineered for what enterprise identity is becoming, not patched onto what it used to be. And we're just getting started.

What you will Do:

• Design, build, and maintain scalable data pipelines that ingest and process identity signals across Oak's Identity Visibility and Intelligence Platform, ensuring high data quality and low latency across the identity graph.
• Develop and extend the core data model that represents human and non-human identities, entitlements, access relationships, and lifecycle events — the foundation every product insight is built on.
• Deliver data features end-to-end, from schema design through to consumption-ready datasets, in close collaboration with product and application engineering teams.
• Architect pipelines and data models for scale, making deliberate design decisions that hold up as the volume of identities, AI agents, and access events grows.
• Partner with product and application teams to translate IAM and IGA domain requirements -access reviews, JIT request flows, identity lifecycle changes - into reliable, well-modeled data products.
  
  

• Hands-on experience designing and operating data infrastructure on at least one major public cloud provider (AWS, GCP, or Azure), with a solid grasp of cloud-native storage, compute, and networking primitives.
• Proficiency with modern workflow orchestration frameworks — Airflow, Dagster, Temporal, or Step Functions — including building reliable, observable pipelines at production scale.
• Deep familiarity with relational databases such as PostgreSQL, and the ability to model and query data correctly under production constraints.
• Experience working with analytical and lakehouse platforms — Snowflake, Databricks, ClickHouse, or Trino/Presto — and a clear sense of when to reach for each one given the workload's latency, scale, and cost profile.
  
  

• Experience with distributed data processing frameworks such as Apache Spark, including designing and optimizing large-scale data pipelines.
• Hands-on experience with log search and analytics platforms such as OpenSearch or Elasticsearch, including index design and query optimization.
• Practical experience integrating large language models into data pipelines for enrichment, classification, or entity resolution — ideally applied to identity or security data.