Senior Data Platform Engineer
Tessera Labs is hiring a Senior Data Platform Engineer to build backend services and APIs for data access, querying, ingestion, and synchronization across enterprise systems. The role centers on Python, SQL, relational databases, and distributed/concurrent workloads, with cloud infrastructure (AWS/Azure/GCP) as a nice-to-have.
- Compensation
- $200K–$300K / yr
- Location
- Remote in the U.S.
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Data engineering
- Posted
Skills
Must have
- At least 4+ years of experience as a Data Platform Engineer
- Strong Python and SQL, with experience building maintainable, tested production software
- Experience developing backend services, data infrastructure, or data platform components, beyond configuring pipelines
- Solid understanding of relational databases, query optimization, data formats, and tradeoffs between querying data in place vs. materializing it
- Experience with concurrent or distributed workloads, including failure handling, resource management, and performance debugging
- Ability to investigate unfamiliar systems and turn complex requirements into clear, practical designs
Nice to have
- Experience with analytical query engines, lakehouse technologies, workflow orchestration, enterprise-system integrations, or containerized infrastructure
- Experience building connector libraries, developer-facing APIs, or shared platforms used by other engineering teams
- Familiarity with cloud infrastructure (AWS, Azure, or GCP), including networking, storage, IAM, and infrastructure as code
Full description
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 4+ 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.
- A solid understanding of relational databases, query optimization, data formats, and the tradeoffs between querying data in place and materializing it elsewhere.