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# Software Engineer - Ingestion Core team

- **Company:** Databricks
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $166,000.0 - $225,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, C++ (Programming Language), Databases, Distributed Systems, Python (Programming Language), Machine Learning, Data Streaming, Data Processing, Apache Spark, Data Lakes, Low Latency, Production Code
- **Published:** September 17, 2026
- **Apply:** https://startup.jobs/sr-software-engineer-ingestion-core-team-databricks-10099777

## About the Role

* 5+ years of experience writing production code in one of: Java, Scala, Go, C++, or Python.
 * Experience architecting, developing, and deploying large-scale distributed and asynchronous systems.
 * Experience with distributed systems, streaming, Spark, databases, data processing, or CDC.
 * Experience building or operating systems where scale, throughput, latency, reliability, and cost are important considerations.
 * Comfortable using AI tools and defining effective evals to shorten the development loop.

Pay Range Transparency

## Description

As an engineer on the team, you will work on projects that:

 * Build distributed infrastructure to ingest data from diverse sources and support streaming ingestion, incremental processing, and replication. This isn't just about building plugin connectors.
 * Reduce end-to-end latency, increase throughput, and reduce costs from the time data appears in source systems to when it is available in Delta Lake.
 * Design and optimize streaming and distributed workloads for throughput, cost, latency, reliability, and scale.
 * Optimize streaming workloads by exploring and applying ML techniques.
 * Build monitoring and observability capabilities (customer-facing and internal) that provide visibility into ingestion workflows and the systems running them.
 * Collaborate with partner teams to enable use cases like RAG and AI agents.
 * Build Agentic SDKs with strong evaluations.


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