Senior-Staff Data Platform Engineer, Health Plan Data

About Helm Health

Ask most people what their health care will cost, and they can’t tell you. In most health plans, what a member owes is worked out after the fact, so they find out at the pharmacy counter or in a bill weeks later, when it’s too late to make a different choice.

Helm is a Series A startup changing that. We build the infrastructure behind Dynamic Copay, a health plan model that shows members a simple, upfront price for their care before they get it, based on the provider they choose and what their plan covers. Health insurance payors partner with Helm to offer simpler plans that help their members find higher-value care at lower cost.

Our team has specialized in Dynamic Copay since 2020, and Helm is the only independent platform in the market. We partner with some of the nation’s largest payors, and the platform we’re building will sit underneath the health benefits of millions of people.

Why this role, why now

Getting the data right is not an internal metric at Helm. It is the product. Every answer Helm gives, whether a provider is in network, whether a member is eligible, or what a visit will cost, is computed from our data platform: provider networks, eligibility files, claims history, and plan design. It is the foundation every engine and every customer commitment stands on.

We’re hiring a Staff/Principal Data Platform Engineer, Health Plan Data to be the technical leader and build the foundation of Helm’s data platform. You’ll own its architecture and technical direction, setting how provider network, eligibility, claims, and plan design data are ingested, modeled, and served to the engines that run on them. The decisions you make in your first six months will shape how Helm’s data layer scales with every payor that follows.

What you will do

• Own the health data platform architecture. Define and drive the target architecture for how data moves through Helm: ingestion, conformance, storage, and serving across provider network, eligibility, claims, and plan design. Set the patterns every team builds on: data contracts, build once and promote, versioned configuration, traceable lineage.
• Build the pipelines that everything else depends on. Design and operate batch and streaming pipelines that ingest payor files, API feeds, and event streams at scale. That means idempotent, replayable, backfill-friendly ingestion with schema validation, quarantine and reconciliation paths, and orchestration you can reason about at 2am. You’ll decide where CDC, event streaming, and scheduled batch each belong.
• Technical leadership across the core domains. Lead design for the hardest data problems in health benefits:• Provider network data: rosters, networks, and directories that power accurate care search
• Eligibility and enrollment: 834 files, census files, and member data that has to be right every cycle
• Claims: multi-payor claims processing and the data behind adjudication and copay validation
• Plan design: benefit configurations and rules that turn a plan document into computable logic

• Build enterprise-grade, audit-ready systems. Our customers are regulated payors, and our bar is theirs. You’ll build for reliability, observability, data quality, and provenance by default: append-only data, auditable change history, and data quality checks at every stage, in a HIPAA-regulated environment on the path to SOC 2 and HITRUST.
• Drive outcomes, not output. You’re accountable for results: platform reliability, data correctness, and predictable delivery. You clear the obstacles in the way, whether or not the code is yours. Partner with product and engineering leadership to turn the roadmap into sequenced, shippable plans.
• Raise the bar around you. At this level, your impact multiplies through other engineers, not just your own commits. You’ll lead design reviews, set the bar for engineering and operational excellence, and mentor the engineers around you.

What you will bring

• Staff+ data architecture experience. 10+ years designing and building data platforms and distributed systems at scale, with a record of technical leadership across teams. You’ve owned cross-cutting data infrastructure and platform decisions, written the design doc, and shipped the result. You favor pragmatic, incremental evolution over big-bang rewrites.
• Expertise in healthcare data. X12 EDI (834, 837, 835), eligibility, claims, provider data, and/or FHIR. You’re comfortable working with PHI under strict data privacy controls, including access controls, de-identification, and audit logging.
• Deep data engineering craft. You’ve built and run production batch and streaming pipelines (think Beam/Dataflow, Spark, Kafka or Pub/Sub, or equivalents), and you know how to make them idempotent, replayable, and cheap to backfill. You’re fluent in dimensional, event-sourced, and effective-dated modeling for both analytical and operational serving.
• Orchestration and transformation experience. You’ve run Dagster, Airflow, or similar in production, and you’ve used dbt, Dataform, or a comparable framework to build tested, documented, version-controlled transformation layers.
• Data quality and observability discipline. You treat data contracts, schema evolution, automated testing, lineage, and freshness and anomaly monitoring as part of the pipeline, not an afterthought.
• Fluency with a modern cloud data stack. GCP preferred (BigQuery, Cloud Run, Pub/Sub, Cloud Storage), along with Postgres, Python, Go, and infrastructure as code. You choose tools pragmatically and can defend the tradeoffs.
• Strong AI skills. You use AI coding tools and agents fluently in your daily work (Claude Code, Cursor, Copilot, or similar) and can show how they’ve changed the way you design, build, test, and ship. You know how to break work into agent-sized pieces, review AI-generated code critically, and keep quality and security high when using these tools.
• Clear communication. Precise writing, async-first habits, and the ability to make complex systems understandable to engineers, product partners, and executives alike.

Nice to have

• Experience building or working on ML or agentic systems (tool-using agents, multi-agent workflows, MCP or similar tool integrations), including evals, observability, and failure modes like hallucination and drift
• Health plan or payor-side experience
• Search and relevance systems
• CDC tooling (Debezium, Datastream)

Internal Tools/Technology

• Cursor / Linear / GitHub / Notion / Whimsical
• Slack / Google Workspace / Zoom
• incident.io / Sentry
• Claude, ChatGPT, Gemini — we are an AI-forward engineering team
• macOS / Linux

Compensation

The target base salary range for this position is $200,000 - $233,000 and is part of a competitive total rewards package including equity and benefits. Individual pay may vary from the target range and is determined by several factors, including experience, skills, location, internal pay equity, and other relevant business considerations.

Benefits/Offerings

• Base salary + bonus + equity
• Unlimited PTO (mandatory 12 days)
• Computer + home office stipend
• 401(k) + matching
• Medical, dental, and vision insurance
• Autonomy and tons of room for career growth

Occasional Travel

We meet quarterly as a company.

Please note that this is a fully remote opportunity.