Data Platform Engineer
US$200,000 – US$250,000 / yr base salary
Posted 11 Sep 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 2+ yrs
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Full-time
| Base salary | US$200,000 – US$250,000 / yr |
|---|
About Tessera Labs
Tessera Labs is redefining how enterprises adopt and operationalize Artificial Intelligence. Backed by Foundation Capital and led by a world-class founding team, we build multi-agent AI systems that automate complex business workflows across platforms such as SAP, Salesforce, Workday, Snowflake, MuleSoft, and more.
Our mission is to bring real AI automation to the enterprise—delivering speed, precision, and measurable impact. We operate with extreme ownership, move quickly, and build at the frontier of applied AI.
We're backed by Andreessen Horowitz and Foundation Capital, and built by leaders from Meta AI, Google Research, Microsoft AI, SAP, Oracle, and Salesforce. We move fast, operate with extreme ownership, and build at the frontier of applied AI. Our ethos in one line: enterprise serious, AI forward.
About the role
We’re looking for a Data Platform Engineer to build the software that connects enterprise systems and makes their data usable across applications, analytics, and AI.
You’ll work at the intersection of backend engineering and data systems, building reusable capabilities for connecting to, querying, moving, and processing data.
This role is open to remote candidates, with a preference for those based in the Bay Area.
What you’ll do
-
At least 2+ years of experience as a Data Platform Engineer
-
Build core data platform capabilities. Develop services and APIs for data access, query execution, ingestion, and synchronization.
-
Make integrations reusable. Build connector frameworks and abstractions that support different systems and access patterns without requiring bespoke implementations.
-
Improve performance and resource efficiency. Identify bottlenecks in querying and data movement; apply partitioning, parallelism, and effective memory and storage management.
-
Own execution reliability. Build orchestration, retries, checkpointing, and observability so workloads recover predictably from failures and maintain data correctness.
-
Take capabilities from design to production. Work with product and engineering teams to define interfaces, make architectural tradeoffs, and operate what you build.
What you’ll bring
-
Strong Python and SQL, with experience building maintainable, tested production software.
-
Experience developing backend services, data infrastructure, or data platform components, beyond configuring pipelines.