Sr. Director, Software Engineering

At H1, we believe access to the best healthcare information is a basic human right. Our mission is to provide a platform that can optimally inform every doctor interaction globally. This promotes health equity and builds needed trust in healthcare systems. To accomplish this, our teams harness the power of data and AI-technology to unlock groundbreaking medical insights and convert those insights into action that result in optimal patient outcomes and accelerates an equitable and inclusive drug development lifecycle.

Visit h1.com to learn more about us.

As part of H1’s hiring process, all candidates are required to participate in an in-person final interview. Depending on your location, this may require travel.

H1's Product Engineering organization is the engine behind the products that put our data in front of the people who need it most — health plans, digital health companies, life sciences organizations, and the clinical teams they serve. We build the client-facing products, APIs, integrations, and intelligent workflows that transform H1's rich underlying data into real decisions: which providers belong in a network directory, where clinical trials should run, and how healthcare organizations can operate with greater accuracy and trust.

Product Engineering at H1 sits at the crossroads of complex data systems and enterprise software delivery, and our work directly drives revenue, client retention, and patient impact. We move fast, we own our products end-to-end, and we hold ourselves to a high bar — because in healthcare, the quality of what we build has consequences.

WHAT YOU'LL DO AT H1

• As Sr. Director of Software Engineering for Data Streams, you will own H1's data feed delivery platform, the system that produces, governs, and delivers structured healthcare data to enterprise customers, primarily in pharma and life sciences. This is not a data engineering role and it is not an infrastructure role. You are owning a data product: the schema contracts, the delivery platform, the QA framework, and the governance discipline that makes H1's data trustworthy to the organizations that depend on it. In healthcare and pharma, data accuracy isn't just a quality metric, it has real consequences for the providers, patients, and clinical decisions downstream from our feeds.
• You will lead the engineering organization responsible for H1's quarterly data feed releases — HCP data, HCO (healthcare organization) data, and clinical trial data, as well as the buildout of DAM (Directory Accuracy Management), our configurable pipeline platform for ingesting customer-supplied provider directory data, running it through H1's accuracy and management processing, and delivering standardized outputs. The vision for DAM is moving from bespoke per-customer engineering to a fully productized, template-driven model where onboarding a new enterprise customer requires configuration, not custom code. You will drive that productization journey from where it is today through to completion.

You will:

• Own the full Data Streams engineering organization, leading engineering managers and senior ICs, setting technical vision, and building a culture grounded in accuracy, governance, and delivery reliability.
• Drive the DAM platform buildout across all phases: Foundation (canonical schemas, entity resolution, S3/SFTP delivery), Configurability (onboarding UI), Operations (alerting, observability), CSV flavors, POC mapping tool, and Enrichment, moving the team from bespoke per-customer engineering to a productized, configurable model.
• Own and enforce the Data Stream Governance framework, the quarterly release cadence, data dictionary contracts, track classifications (A/B/C), QA gates, and hotfix policy, ensuring every change shipped matches its documentation exactly and every release earns customer trust.
• Partner with Product, the Data Feeds Lead, Commercial, and Customer Success to align on schema decisions, new data sources, and the delivery roadmap and serve as the senior engineering voice in customer conversations about data contracts, delivery commitments, and onboarding timelines.
• Manage competing priorities across the engineering org, PM, CS, and sales, communicating tradeoffs clearly and making calls when schema changes, delivery timelines, or customer commitments conflict.
• Build and develop the engineering team, hiring, coaching, performance management, and developing engineering managers who can lead with the same discipline and accuracy-first instinct the platform requires.
• Drive triage and incident response for data delivery issues with urgency and transparency — owning root cause analysis, customer communication, and the QA gate improvements that prevent recurrence.
• Stay close enough to the technology to engage meaningfully in architecture and system design decisions — you won't be coding daily, but your engineers trust that when you weigh in, it's grounded in real understanding of how data moves through a governed delivery pipeline.

ABOUT YOU

You have spent your career at the intersection of data and delivery and you know the difference between building a data pipeline and owning a data product. You understand that when an enterprise customer receives a file where a field the data dictionary said would be non-nullable is null for 30% of records, the problem isn't just a bug,  it's a broken contract. And you've been the person who owns that conversation, drives the root cause, fixes the QA gate, and makes sure it doesn't happen again.

You are a governance-first leader who finds schema contracts and delivery discipline genuinely interesting, not just necessary.

You've moved a team through productization before, from bespoke per-customer engineering to a configurable, template-driven model, and you know how hard that organizational and technical change is in practice. You've managed other engineering managers and know that developing a manager is a different skill from developing a senior IC.

You are comfortable in commercial conversations with pharma and life sciences customers, and you have enough domain knowledge to be credible when the discussion turns to how their organizations consume and use external data feeds.

You thrive in environments where accuracy is the value proposition and the expectation is that you build the structure rather than wait for it. You are a clear communicator, a decisive decision-maker, and the kind of leader who makes their team feel the difference between delivering data and delivering data that can be trusted.

REQUIREMENTS

• 12+ years of software engineering experience with 4+ years leading engineering organizations at the Director or Sr. Director level, including direct management of engineering managers.
• Proven track record owning a data product delivered to external enterprise customers — you have owned the schema, the delivery contract, and the customer relationship, not just the pipeline that produces the data.
• Experience driving productization: moving from bespoke per-customer engineering to a configurable, template-driven platform. You have done this before and can speak to what it takes organizationally and technically.
• Deep understanding of enterprise data delivery, schema governance, data dictionaries, file format expertise (Parquet, CSV, Avro), S3 and SFTP delivery, versioning, and release management for data products.
• Experience with QA frameworks for data delivery, schema conformance, completeness validation, value integrity checks, and pass/fail-with-reasons reporting. You treat the data dictionary as the contract and you've enforced that standard.
• Pharma or life sciences domain knowledge, you understand how pharma and life sciences organizations consume external data feeds (HCP data, clinical trial data, HCO data) and what their expectations are around schema stability, delivery timing, and data quality.
• Manager of managers experience, you have developed engineering managers as direct reports and know how to coach leadership, not just technical execution.
• Strong cross-functional communication: you can represent engineering in commercial conversations with pharma customers, data contract discussions with CS, and executive roadmap reviews with clarity and credibility.
• Decisive under ambiguity, you own tradeoffs between schema changes and customer commitments, between delivery speed and governance discipline, and communicate them clearly to all stakeholders.