Senior Engineer, Data Services

Why TrueML? TrueML is a mission-driven financial software company that aims to create better customer experiences for distressed borrowers. Consumers today want personal, digital-first experiences that align with their lifestyles, especially when it comes to managing finances. TrueML’s approach uses machine learning to engage each customer digitally and adjust strategies in real time in response to their interactions. The TrueML team includes inspired data scientists, financial services industry experts and customer experience fanatics building technology to serve people in a way that recognizes their unique needs and preferences as human beings and endeavoring toward ensuring nobody gets locked out of the financial system.

About the Role 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.

Technical Delivery: 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. Technical Design & Architecture: 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. Event & Streaming Infrastructure: 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. Schemas, Contracts & Infrastructure as Code: 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. AI-Assisted Engineering: 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. Data Reliability: 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. Mentorship & Technical Leadership: 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. Cross-Functional Collaboration: 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. Engineering Standards: 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.

Education & Experience: Bachelor's degree in a related technical field, or equivalent practical experience. Track Record: 5+ years of software or data engineering experience, with demonstrated ownership of production systems. No people management requirement. Technical Mastery: 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. AI & Modern Tooling: 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. Data Handling Judgment: 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. Ownership Mindset: 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. Communication: You write clear TDDs and design proposals. You can explain technical trade-offs to non-engineers and flag dependencies across teams early.