Forward Deployed Analytics Engineer

Job description

## Why Translucent Healthcare providers drive $2.5 trillion in medical expenditures annually — and operate on razor-thin 2–5% margins. Despite these stakes, the finance teams behind these organizations are buried in spreadsheets, manual data pulls, and disconnected systems, spending more time finding and cleaning data than actually using it to make decisions. Translucent is changing that. We're building the agentic AI platform designed exclusively for healthcare finance — giving every finance team, department, service line their own arsenal of AI Agents that run 24/7, understand their specific data, business logic, and workflows. Founded in 2024 and backed by GV, NEA, FPV, and Virtue, we've already been deployed by healthcare organizations managing over $5 billion in combined revenue. The product-market fit is real, the problem is massive, and we're just getting started. If you want to work at the intersection of AI and one of the most complex, consequential industries in the world — this is the place. ### About the role We're hiring a Forward Deployed Analytics Engineer to join our team in New York. You will sit at the intersection of our customers' financial and clinical data and the agentic AI systems we are building on top of it. Working within our managed data pipeline, you will reverse-engineer customer-specific business rules, build the transformations that map raw customer data into our unified ontology and semantic layer, and hold the bar for correctness on everything that flows through it. This is a high-ownership role. You will work directly with customers to understand their source-of-truth financial and clinical models, then translate that understanding into durable, well-tested data transformations. ### What you'll do - Understand customer requirements, current processes, workflows and business logic. - Identify the data required to stand up a customer workspace, and review customer source-of-truth financial and clinical data models — potentially on-site with customers to capture business logic firsthand. - Reverse-engineer and codify customer-specific business rules, from payer contract logic to chart-of-accounts idiosyncrasies. - Build and validate transformations that map customer data into Translucent's unified ontology and semantic layer, using SQL within our managed data pipeline. - Own testing and validation rigor for every transformation you ship — you are the last line of defense on data correctness. - Partner with our data platforming team when transformation needs surface pipeline or platform constraints, without owning that infrastructure yourself. - Interact directly with customers to confirm data access and validate your understanding of their source systems and business logic. - Work with our platform team to identify expansion opportunities for core infrastructure and shared services, based on patterns you see across customer engagements. - Partner with our insights and product engineering teams to understand and expand our data ontology as new customer needs and data sources emerge. ## What we're looking for ### Must-haves - Deep healthcare data domain experience — prior work at a health system, healthcare-focused consulting, or health tech, with hands-on exposure to claims, reimbursement, or revenue cycle data. (EHR/clinical data experience is a plus) - Comfort reverse-engineering messy or under-documented business logic directly from source systems. - Strong SQL — this is the primary tool of the role, and we expect real fluency, not familiarity. Bonus points for BigQuery SQL experience. - Experience working inside a modern data platform (Databricks, Snowflake, Fabric, BigQuery, or similar) as a hands-on user. - Experience with dbt-style transformation tooling (we use SQLMesh) including model contracts and layered/medallion architectures; comfort working in transformation-as-code workflows including testing and validation. - Track record of owning a data pipeline or transformation layer end-to-end, not just contributing to one someone else built. - Solid data modeling and pipeline instincts: comfortable reasoning about schemas, normalization vs. denormalization, modeling complex domain entities and operating a data pipeline. - Experience reasoning about and building both historical backfills and incremental loads — knowing when each is appropriate, and how to handle late-arriving or corrected data without breaking downstream consistency. - Comfortable operating in a git-native, PR-driven workflow — version control, code review, and shipping changes through an established CI/CD pipeline. - Production Python experience — you've shipped and maintained Python in a real pipeline or codebase, not just used it for scripts. - 3-5+ years of relevant industry experience. ### Nice-to-haves - Epic Cogito and Epic Clarity certifications or accreditations - Comfort working under strict PHI/data-governance constraints (e.g., synthetic-only test fixtures, no real patient data in code). - Healthcare interoperability standards experience (HL7, FHIR, X12 837/835). - Experience working directly with customers or stakeholders in a forward-deployed, integrations, or solutions engineering capacity. - Hands-on experience with GCP BigQuery SQL dialect ### Education Bachelor's degree (or higher) in a quantitative or business-adjacent discipline — computer science, statistics, mathematics, health informatics, or finance/accounting — or equivalent experience in healthcare data, revenue cycle, or health system finance. We weigh hands-on healthcare data experience and SQL fluency at least as heavily as formal degree background. ### Location Union Square, New York City. **In-office 4 days per week.** ### Compensation $150k - $200k