Senior Data Platform Engineer

Proven experience designing and operating data platforms at scale - warehouse, data lake, or lakehouse architectures in production.

Hands-on experience with a modern lakehouse table format - Iceberg strongly preferred; Delta Lake or Hudi also welcome. You understand how the format works under the hood: metadata layout, snapshots, manifests, compaction, copy-on-write vs. merge-on-read.

Clear mental model of catalogs (REST, Polaris, Glue, Unity, Hive) - their trade-offs, and how compute stays detached from storage.

Exposure to at least one vendor lakehouse or query platform - Snowflake, Starburst, or Databricks — at the level where you can reason about its architecture, not just use its UI.

Strong experience with a distributed processing engine - Flink strongly preferred; Spark also fine. You can reason about its internals, fine-tune a running job, and debug a pipeline that’s silently degrading.

Familiarity with durable execution - Temporal, Restate, or similar - or at minimum a solid mental model of what durable execution means and why it matters for data workflows.

Production experience building and operating APIs (REST or gRPC) at scale - good instincts about contracts, versioning, retries, rate limiting, and observability.

Solid understanding of Kafka and event-driven architectures (producers/consumers, partitioning, delivery semantics).

Comfortable in regulated environments (healthcare, fintech, gov) where audit, compliance, and data governance are part of every design.

Platform mindset: you design for self-service, API-first, and with systems and agents - not only humans - as legitimate consumers.

Bonus

Deeper familiarity with open/REST catalogs (Polaris, Nessie, Unity) beyond basic use.

Observability stack fluency (Prometheus, Grafana, OpenTelemetry).

Prior work on agentic or AI-facing API surfaces, or MCP-style interfaces.

Experience in HIPAA, FedRAMP, or SOC 2 environments.

dbt, DataHub, or data contract tooling exposure.

Mindset and Collaboration

Service orientation: you build APIs (and increasingly agent-facing tools) that others love to use.

Reliability-first: failure modes, retries, and observability are part of day-one design.

Cross-functional: you enjoy working with data engineers, analysts, and ML engineers and understanding their problems.

Documentation mindset: good APIs come with great docs — and good docs now means machine-readable too.

Iterative: you ship incrementally and improve based on feedback.