Java Backend Technical Lead

Roles & Responsibilties

Build and maintain production services in Java + Spring Boot — REST APIs, background workers, and event consumers that expose capability APIs.

Design clean REST endpoints with clear contracts, structured errors, OpenAPI docs, and versioned response shapes.

Work with Postgres, DynamoDB, Redis, and SQS/SNS on AWS; implement authN/authZ and tenant scoping.

Ship with tests and CI/CD; participate in code review and on-call for services the team operates.

Move into Python for tooling, scripts, or services when the work requires it — with team support if you are ramping up.

Use AI coding agents and MCP tools actively as part of your workflow — always with review.

Code you write, tests you write, features you ship. On-call responsibility for services the team operates.

Must-have skills

Production experience in Java and Spring Boot — not just tutorials. Comfortable with Spring Web, Spring Data, dependency injection, config management, and Spring Security basics.

REST API design fundamentals resource naming, HTTP semantics, status codes, error contracts, pagination.

SQL and relational database experience — Postgres or equivalent, joins, indexes, transactions.

Basic security awareness input validation, safe handling of secrets and credentials, no secrets in code or logs, OWASP top-10 literacy.

Basic OAuth2 / OIDC awareness what tokens are, how a login flow works at a high level, what an access token vs. refresh token is for.

Basic distributed-systems awareness what retries, timeouts, and idempotency mean and why they matter.

Unit and integration testing (JUnit + Spring Boot Test).

Git, code review, and CI/CD basics.

AWS basics — or genuine willingness and ability to pick it up quickly.

Willingness to work in Python when the work requires it — even if your Python is not yet strong.

Willingness and appetite to learn new capability areas as the platform grows.

Active use of AI coding agents in day-to-day work.

Nice to have

Python at production depth — real Python services you have shipped, not just scripts. FastAPI or Flask familiarity.

Python for ML- and data-adjacent work — data pipelines, embedding services, feature stores.

Reactive programming with Spring WebFlux / Reactor — especially valuable for streaming or high-concurrency capabilities.