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Tessera Labs is building multi-agent AI systems that automate complex business workflows across enterprise platforms including SAP, Salesforce, Workday, Snowflake, and MuleSoft. The company is backed by Andreessen Horowitz and Foundation Capital, with a founding team drawn from Meta AI, Google Research, Microsoft AI, and major enterprise software vendors.
As a Data Platform Engineer, you will build the software infrastructure 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, developing reusable capabilities for connecting to, querying, moving, and processing data.
Key responsibilities include:
- Develop core data platform capabilities: build services and APIs for data access, query execution, ingestion, and synchronization
- Create reusable integrations: design connector frameworks and abstractions that support different systems and access patterns without bespoke implementations
- Optimize performance and resource efficiency: identify bottlenecks in querying and data movement; apply partitioning, parallelism, and effective memory and storage management
- Ensure execution reliability: build orchestration, retries, checkpointing, and observability so workloads recover predictably from failures and maintain data correctness
- Drive capabilities from design to production: collaborate with product and engineering teams to define interfaces, make architectural tradeoffs, and operate what you build
REQUIREMENTS:
- Minimum 2+ 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 versus materializing it elsewhere
- 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
