Data Platform Engineer

Tessera Labs

2d•$200,000 - $250,000•Remote

About The Position

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.

Requirements

At least 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.
A solid understanding of relational databases, query optimization, data formats, and the tradeoffs between querying data in place and materializing it elsewhere.
Experience with concurrent or distributed workloads, including failure handling, resource management, and performance debugging.
The ability to investigate unfamiliar systems and turn complex requirements into clear, practical designs.

Nice To Haves

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 is especially relevant.
Familiarity with cloud infrastructure (AWS, Azure, or GCP), including networking, storage, identity and access management (IAM), and infrastructure as code.

Responsibilities

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.