Sr. Product Manager, Data Integrations

Lively•Boise, ID

2h•$165,000 - $195,000•Remote

About The Position

Lively is hiring a Senior Product Manager to own how data moves between our platform and the systems our customers and partners rely on — payroll and HR platforms, benefits administration systems, carriers, and third-party administrators. You will own enrollment and eligibility data, file-based and API-based exchange, and the integration roadmap that connects Lively to its ecosystem. You will also own the AI agent experiences that sit on top of that data layer, with a focus on onboarding and validation: agents that help customers and partners get their data into Lively correctly the first time, interpret what arrives, surface problems in plain language, and propose corrections. Integration work at Lively is increasingly AI-assisted, and this role decides where that assistance belongs and where human review stays mandatory. Alongside this, you will shape Lively's forward data integration strategy: how our connectivity model evolves as the ecosystem moves toward real-time exchange, where we invest in building versus partnering, and how we make each additional integration cheaper and faster than the last. This is a technically grounded product role. You will work closely with engineering on system behavior, data design, and agent behavior, and you will be the person partners and internal teams come to when they need to understand how the data actually flows. You will own large initiatives end-to-end and operate as the primary subject matter expert for your areas. Please note: As a majority of our engineering team sits in Pacific time, candidate preference will be given to those in the Pacific and Mountain timezones.

Requirements

5+ years of product management experience, with meaningful time owning integrations, data exchange, or platform APIs as a primary product surface
Experience owning the exchange of structured business data between organizations — whether by file, API, or both — including the formats, cadence, and correctness guarantees each side depends on
Demonstrated ownership of API products, including how they are designed, documented, evolved, and eventually retired without disrupting the partners built on them
Strong judgment about data reliability at scale — you think in terms of what happens when a feed is late, incomplete, duplicated, or wrong, and you design for those cases deliberately
Experience working directly with external technical partners — you can hold a substantive conversation with a partner's engineering team and represent Lively's position credibly without an engineer in the room
Technical depth sufficient to be self-sufficient — you investigate data questions yourself rather than routing every one to engineering, and engineers regard your specifications as complete
Experience handling sensitive personal or financial data under a regulated framework, with an instinct for privacy, security, and auditability
Excellent analytical and problem-solving skills, with the ability to synthesize complex data into clear product decisions
Outstanding communication skills — you can align engineers, partners, and executives on a shared direction
Strong familiarity with Agile methodologies and standard product tooling
Bachelor's degree in a technical or business field, or equivalent practical experience

Nice To Haves

Benefits or health insurance domain experience — HSA/FSA/HRA plans, benefits compliance, IRS regulations, or the administration workflows behind them
Ecosystem familiarity with payroll providers, HR platforms, benefits administration systems, carriers, or third-party administrators
Fluency with industry data standards used in benefits, insurance, healthcare, or financial services exchange
Experience modernizing a legacy integration surface — migrating partners onto a new model without breaking the old one
Experience evaluating integration and connectivity vendors, and owning the build/buy/partner decision
Experience building AI-powered product experiences, including designing for reliability and user trust
Experience spanning both B2B (employer/broker-facing) and B2C (employee-facing) products
Leadership experience, including mentoring or developing junior PMs
Growth mindset and high self-motivation — you identify the next problem rather than waiting for it to be assigned

Responsibilities

Own the data exchanged between Lively and its partners: what moves, in what form, on what cadence, and what happens when something goes wrong. Define the contracts that both sides build against.
Own the AI agent experiences that accelerate getting partner and customer data into Lively: interpreting unfamiliar file structures, mapping fields, catching errors before they propagate, explaining what went wrong in language a non-technical administrator can act on, and recommending fixes. Define where the agent acts, where it advises, and where a human must approve.
Own the enrollment data lifecycle end to end, including how changes, corrections, and exceptions are handled, and how Lively proves its records match the partner's.
Own the non-functional requirements of Lively's data integration platform: throughput, latency, availability, recovery, and the guarantee that data is never silently lost or partially applied. Define how failures are detected, communicated, and resolved. Plan capacity for the benefits industry's seasonal enrollment peaks and for growth in partner and record volume. Set the accuracy bar AI-assisted validation must clear before production use.
Set the multi-year direction for how Lively connects to its ecosystem: the target architecture, the migration path from today's model, the role AI plays in reducing integration effort, and the build/buy/partner decisions that get us there.
Translate partner needs, customer commitments, and regulatory constraints into clear, complete requirements — including the edge cases and failure paths, not only the intended flow.
Support the technical onboarding of new partners from specification through go-live, and reduce the time and effort each new connection takes.
Act as the primary SME for your areas, fielding questions from implementation, customer success, sales, and leadership. Communicate vision, roadmap, and progress to internal and external audiences.
Sequence complex, multi-quarter integration work into delivery milestones, balancing customer commitments, partner dependencies, and platform health.
Define and monitor the metrics that describe integration health, onboarding effort, and AI agent accuracy, and use them to direct investment.
Use AI tooling in your own product work — research, specification, data analysis, prototyping — and raise the bar for how the product organization does the same.
Track how the benefits, payroll, and data-interoperability landscape is evolving, including where AI is changing what integration work costs, and identify partnership and standardization opportunities.