Senior Engineer, Data Services

TrueML

1d•$140,000 - $180,000•Remote

About The Position

As a Senior Engineer on the Data Services team at TrueML, you will be a key technical contributor responsible for the reliability, scalability, and evolution of TrueML's event and data infrastructure — including the centralized event bus that carries asynchronous communication between domains, the streaming and change-data-capture pipelines that move operational data into our analytics platforms, and the integration services that bridge our modern platform with legacy systems through an active migration. You will take full ownership of complex technical work, set a high engineering bar, and actively help elevate the engineers around you. This is a team that builds with AI rather than just alongside it — we run LLM-backed services in our own pipelines, and our repositories ship agent-readable context and tooling. In partnership with your Engineering Manager, Product, and sibling engineering teams, you will translate requirements into well-architected solutions and bring a strong bias toward reliability, observability, and measurability.

Requirements

Bachelor's degree in a related technical field, or equivalent practical experience.
5+ years of software or data engineering experience, with demonstrated ownership of production systems.
Deep experience with event-driven architecture — event buses, schema evolution, idempotency, dead-letter handling, replay.
Hands-on with a managed event platform (EventBridge, SNS/SQS, Kinesis) and/or Kafka with a schema registry.
Strong multi-account AWS fundamentals including Lambda, IAM, and the data ecosystem (S3, Glue Catalog, Athena, Firehose, Iceberg).
Production-grade Go or Typescript, plus willingness to work in the JVM where our legacy platform lives.
Terraform at production scale.
Agentic coding tools (Claude Code or equivalent) as a daily driver, with grounded opinions on their limits.
Practical understanding of MCP — consuming servers, ideally building one — and of packaging recurring work into reusable skills and workflows.
Hands-on experience building something real on an LLM API (Bedrock, Anthropic, or similar): prompt and context engineering, structured output, tool calling, evaluation and guardrails.
You reason about model tiers, token budgets, and dollars, and can defend the cost profile of what you build.
Experience with PII, tokenization, and least-privilege access in a regulated environment — extending to what data reaches a model, and treating prompt injection and over-broad agent permissions as real risks.
You take initiative, follow through without close direction, and treat production reliability as a personal standard — not just a checklist.
You're comfortable inheriting under-documented systems and making them better.
You write clear TDDs and design proposals.
You can explain technical trade-offs to non-engineers and flag dependencies across teams early.

Nice To Haves

No people management requirement.
Building an MCP server.
Maintaining a reliable internet connection and a professional work environment is expected.
The ability to protect confidential company, employee, customer, and business information while working outside of a company office is also required.

Responsibilities

Own complex engineering tickets end-to-end — from design through implementation, testing, and production.
Write clean, well-tested, maintainable code and hold that standard in review.
Author and review Technical Design Documents (TDDs) and contribute to Architecture Decision Records (ADRs).
Lead technical discussions within your domain and help the team make sound, well-documented trade-offs.
Build and maintain the Common Event Bus — event validation and enrichment, EventBridge routing and cross-account targets, DLQ handling and replay, and the connector that lands events into our Iceberg lakehouse — alongside the Kafka/Debezium CDC pipelines and Benthos jobs that serve downstream consumers.
Steward event and API contracts in trueml-model (OTDS) — schema definitions, generated bindings, versioning and compatibility — and treat infrastructure as a first-class deliverable, writing Terraform deployed via Atlantis across sbx/dev/stg/prd, including the reusable modules other teams depend on.
Use AI coding agents as a primary part of your workflow and improve how the team uses them — maintaining repo context and skill files, building and consuming MCP servers that expose our tools safely, and codifying investigation-heavy work into reusable agent workflows.
Help own our LLM-backed services, including prompt and context design, structured output and tool calling, validation loops, and model selection — managing token budgets and cost with the same rigor as any other infrastructure spend.
Own the reliability and operational health of your systems.
Instrument pipelines with meaningful observability — delivery rates, validation failures, DLQ depth, latency — and drive improvements when the signal warrants it.
Actively support the growth of more junior engineers through code review, pairing, and direct feedback — including raising the team's effectiveness with AI tooling.
Lead spikes and timeboxed discovery work within your domain.
Partner with Data Engineering, Data Science, and Product to define clear data contracts, coordinate schema changes, onboard new publishers and subscribers to the bus, and ensure upstream event quality supports downstream consumers.
Contribute to the team's code quality, testing, and operational readiness standards, and help reduce the long tail of under-documented inherited systems.
Participate in FinOps conversations where infrastructure and AI costs touch your systems.