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Tessera Labs is building multi-agent AI systems that automate complex business workflows across enterprise platforms like SAP, Salesforce, Workday, and Snowflake. 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 Senior Data Platform Engineer, you will design and 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, creating reusable capabilities for data access, querying, movement, and processing.
Key responsibilities include:
- Develop core data platform capabilities: services and APIs for data access, query execution, ingestion, and synchronization
- Build connector frameworks and abstractions that support different systems and access patterns without requiring custom implementations for each integration
- Optimize performance and resource efficiency by identifying bottlenecks in querying and data movement, applying partitioning, parallelism, and effective memory/storage management
- Ensure execution reliability through orchestration, retries, checkpointing, and observability so workloads recover predictably from failures and maintain data correctness
- Own the full lifecycle from design to production, working with product and engineering teams to define interfaces, make architectural tradeoffs, and operate systems in production
The role emphasizes extreme ownership, moving quickly, and building at the frontier of applied AI. You'll have direct influence on product direction and work with a deeply technical, customer-focused team solving complex real-world enterprise problems.
REQUIREMENTS:
- Minimum 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 pipeline configuration)
- Solid understanding of relational databases, query optimization, data formats, and tradeoffs between querying in place vs. materializing data elsewhere
- Experience with concurrent or distributed workloads, including failure handling, resource management, and performance debugging
- Ability to investigate unfamiliar systems and translate complex requirements into clear, practical designs
NICE TO HAVE:
- Experience with analytical query engines, lakehouse technologies, workflow orchestration, enterprise-system integrations, or containerized infrastructure
- Background 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
