Working closely with our data engineers, you’ll turn those priorities into well-scoped projects with clear milestones, dependencies, and acceptance criteria. Your remit will span our ingestion engine, data quality and traceability, and integrations that embed Leap into client and partner workflows. Engineering owns architecture and implementation; you’ll help define the capabilities we need and sequence investments alongside immediate business commitments.
This is a hands-on technical product role. You should be comfortable querying BigQuery, reading Airflow DAGs and dbt models, evaluating a Fivetran connector, and investigating a malformed eligibility file. You bring the technical judgment and product discipline to clarify scope, challenge assumptions, and keep delivery on track.
You’ll work closely with Data Engineering, Platform Engineering, Analytics, Implementation, and Client Success. This role sits within Leap’s Engineering organization.
KEY RESPONSIBILITIES
INTAKE & CROSS-TEAM PRIORITIZATION
Own the intake process for Data Engineering, giving teams a clear way to submit requests and visibility into how decisions are made.
Work with requesters to understand the problem, business impact, urgency, and downstream dependencies before committing engineering capacity.
Reconcile competing needs across teams, distinguish firm deadlines from preferred timing, and make trade-offs explicit.
Maintain a single prioritized backlog and roadmap that balance client launches, business needs, platform improvements, and reliability.
Resolve conflicting requests with stakeholders and bring decisions that require leadership input forward with clear options and a recommendation.
Keep teams informed about what is committed, what is deferred, and why. Revisit priorities as needs change, with a clear view of the impact on existing commitments.
Continually improve intake so that gathering requirements and reaching decisions require less back-and-forth as Leap grows.
PRODUCT OWNERSHIP & DELIVERY
Translate prioritized needs into clear problem statements, requirements, dependencies, and measurable acceptance criteria.
Work with engineers to break projects into deliverable milestones, test assumptions, and establish realistic timelines.
Coordinate dependencies across teams and external partners so work is ready to proceed when engineering capacity is available.
Track delivery, surface risks early, and agree on changes to scope or timing with the teams affected.
Validate completed work against acceptance criteria and confirm it solves the original business need.
DATA PLATFORM & RELIABILITY
Maintain a working understanding of the data stack: how data moves through it, which teams depend on it, and where ownership or capabilities are unclear.
Own product requirements for a reusable ingestion engine, including validation, normalization, error handling, and onboarding workflows.
Identify recurring manual work and one-off implementations, and prioritize shared capabilities that reduce the effort required to support growth.
Support engineering-led architecture and tooling decisions by clarifying business requirements and weighing implications for delivery, reliability, cost, and future needs.
Define requirements for data provenance: where data comes from, how it is transformed, where it is used, and who is responsible for it.
Establish expectations for freshness, completeness, and accuracy with data consumers, and work with engineering on monitoring, alerting, and escalation.
Ensure sources, data contracts, ownership, and key platform decisions are documented; drive recurring data quality issues toward durable resolution.
Partner with Analytics on metric lineage and with Security on requirements for access controls, PHI handling, retention, and third-party data use.
CLIENT & PARTNER INTEGRATIONS
Scope eligibility, claims, clinical, drug pricing, and operational data integrations, including formats, delivery methods, refresh cadences, and downstream requirements.
Partner with Implementation to make client onboarding faster and more repeatable, with clear readiness criteria and ownership at each step.
Identify and scope opportunities to embed Leap into client and partner tools through APIs, data exchanges, and workflow integrations.
Work with external technical teams to resolve requirements, confirm dependencies, and coordinate testing and launch.
Validate that integrations work end to end and meet the needs of the teams and workflows that depend on them.
WHAT SUCCESS LOOKS LIKE
Teams know how to request work, what information is needed, and how prioritization decisions are made.
Competing requests are resolved promptly, with clear trade-offs and fewer rounds of clarification.
Engineering works from an agreed set of priorities, with clear scope, known dependencies, and measurable acceptance criteria.
Teams can plan around credible delivery commitments, and changes are communicated early.
New clients and data sources launch through increasingly standardized processes.
Data sources, transformations, and ownership are documented and traceable.
Platform investments reduce manual work, recurring issues, and the effort required to support growth.
Integrations make Leap a more connected part of client and partner workflows.
QUALIFICATIONS
REQUIRED
Experience owning technical products or programs involving data platforms, infrastructure, or integrations—as a technical product manager, technical program manager, solutions architect, or engineer with product ownership responsibilities.
Experience managing intake and prioritization across multiple teams with competing needs, including resolving disagreements, negotiating scope, and building alignment around what gets done first.
A track record of taking ambiguous needs through scoping, prioritization, and delivery, including managing dependencies and making difficult trade-offs.
Strong SQL skills and enough fluency in Python, orchestration tools, and transformation frameworks to investigate issues and challenge technical assumptions.
Experience with cloud data warehouses such as BigQuery or Snowflake, ingestion tools such as Fivetran, and workflows built with tools such as Airflow and dbt.
Experience delivering external file-based or API integrations with incomplete documentation, inconsistent data, and changing requirements.
The ability to reason about system architecture and explain how technical choices affect delivery, reliability, cost, and future capabilities.
Clear writing: you produce specs engineers can build from and documentation other teams can use.
Practical experience using AI tools to accelerate analysis, technical investigation, documentation, or delivery, with sound judgment about validating outputs and handling sensitive data.
PREFERRED
Experience with healthcare eligibility, medical or pharmacy claims, clinical data, or drug pricing.
Familiarity with healthcare data standards such as X12, NCPDP, or HL7/FHIR.
Experience working with PHI in a HIPAA-regulated environment.
A background in data, analytics, implementation, or software engineering.
Experience building reusable integration capabilities or embedding products into partner workflows.
Experience establishing product and delivery practices at an early-stage company.
At Leap, we’re building an outlier company with real impact — and that takes focus, energy, and commitment. If that excites you, we’d love to hear from you.
Leap is an equal opportunity employer and welcomes applicants from all backgrounds. We’re committed to building a team that reflects a diversity of perspectives, experiences, and identities.
