Lead Software Engineer - Python

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Design, build, and maintain the backend services, APIs, data-platform automation, and AI agents behind our data products. This is a backend-heavy role: distributed Python services, streaming LLM/agent runtimes on AWS, RAG pipelines, and the dbt/Airflow automation that powers our data products.

Responsibilities

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• Design, build, and maintain backend services and APIs in Python — RESTful and streaming (SSE) endpoints, agent runtimes on AWS Bedrock AgentCore / ECS Fargate, and event-driven Lambda handlers.
• Architect service boundaries and data flows: define contracts between services, model persistence, and manage state, caching, and asynchronous/background processing.
• Build RAG pipelines and tool-calling AI agents over our data products: retrieval, orchestration, grounding, and evaluation.
• Design data access and storage layers: schema/data modeling, query performance, connection/session management, and integration with warehouses (Snowflake) and key-value stores (DynamoDB).
• Implement auth and identity: OAuth/OIDC flows (per-user 3LO, token vaulting, session binding), least-privilege IAM, secrets management.
• Build and maintain data-platform automation: dbt models, MWAA/Airflow orchestration, and tooling for discoverable, governed, consumable data products.
• Own service reliability and delivery: Terraform, GitHub Actions CI/CD, container builds, structured logging, metrics/tracing, alerting, and cost controls.
• Set technical direction: system and API design, code review, and mentoring.

Required skills

Backend engineering

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• 5+ years designing, building, and maintaining production backend services at scale. 10+ years if Lead level engineering candidate.
• Expert-level Python for server-side development; solid grasp of at least one web/async framework (e.g. aiohttp, FastAPI, Flask) and the WSGI/ASGI model.
• Service and API design: REST (and/or gRPC), request/response and streaming patterns, pagination, versioning, idempotency, and backward-compatible contracts.
• Data layer: SQL and data modeling, query optimization and indexing, transactions, connection pooling; experience with relational, warehouse (Snowflake), and NoSQL/key-value (DynamoDB) stores.
• Server-side patterns: caching strategies, background jobs/workers, queues and event-driven processing, rate limiting, retries/backoff, and timeouts.
• Performance & reliability: profiling, load handling, latency/throughput trade-offs, graceful degradation, and designing for failure.