In this role, you will:
Our data pipelines run faster, scale cleaner, and break less – because you own the architecture and optimization of our BigQuery warehouse end-to-end.
High-throughput processing and real-time analytics become possible at a scale we haven't reached yet – because you're shaping the distributed systems that get us there.
Data capabilities land in the hands of the people who need them – data scientists, engineers, and product stakeholders from problem to solution, not as a downstream dependency.
The infrastructure gets more reliable, more automated, and easier to monitor – because you treat DevOps and MLOps as part of the job, not someone else's problem.
AI/ML models move from development into production and stay there – not handed off, but owned through the full deployment lifecycle.
Must have:
4+ years building high-scale data pipelines and managing cloud data warehouses (BigQuery strongly preferred)
Hands-on experience with Kafka, CDC tools (Estuary or similar), and pipeline orchestration (Airflow, dbt)
Deep command of SQL and NoSQL ecosystems – Postgres, Redis, Elastic
Solid backend development skills with strong OOP/OOD fundamentals
Exposure to MLOps and production AI/ML model deployment
Experience with AI-assisted development tools (Claude Code, GitHub Copilot, Cursor)
Comfortable working independently with minimal structure – you drive things, you don't wait for them
Good to have:
Familiarity with DevOps and async systems: Pulumi/Terraform, RabbitMQ, Docker, WebSockets, Linux
Experience with routing and navigation algorithms










