Senior Technical Program Manager, Productivity Engineering (Data Platform · AI Transformation)

10x Genomics•Pleasanton, CA

9d•$164,100 - $222,100

About The Position

10x Genomics is seeking a Senior Technical Program Manager (TPM) to join their AI-first technology stack initiative. This role is central to building a modern platform and requires a candidate who thrives in ambiguity, can create plans from scratch, and understands the foundational role of data programs in enabling AI. The TPM will own the roadmap for AI/Data Platform and integration programs, with the expectation of expanding scope as the organization grows. The ideal candidate has experience building processes in environments where they didn't previously exist, takes ownership of end-to-end outcomes, and is energized by a flat organizational structure with direct reporting lines to leadership.

Requirements

5+ years of TPM experience with demonstrated ownership of complex, multi-stakeholder programs.
Meaningful time at a mid-stage startup (Series A–D), or equivalent experience as a founder or early employee — you know what it means to build without a safety net.
Hands-on delivery across data infrastructure or data platform programs: pipelines, warehousing, ELT, real-time data flows.
Fluency in modern integration patterns — you can hold a substantive conversation with engineers about event-driven architecture, API design, and middleware retirement without needing a glossary.
Proven ability to build the roadmap, not inherit one. You have done this before and have the scars to show it.
Strong written and verbal communication — you can write a crisp exec update and a detailed program brief and know when each is appropriate.

Nice To Haves

Experience driving AI or ML programs: NLQ, model deployment pipelines, feature stores, or GenAI product integrations.
Familiarity with the 10x stack or equivalent: Snowflake, Airflow, Kafka, dbt, AWS, Oracle Fusion.
Background in life sciences, genomics, or regulated data environments.
Worked cross-functionally across Engineering, Product, Finance, and Operations simultaneously — not sequentially.

Responsibilities

Own and drive the roadmap across AI / Data Platform and Finance integration programs; expand scope as the business grows.
Take programs from zero to shipped — scoping, sequencing, dependency management, risk visibility, and retrospective.
Translate ambiguous business problems into structured, time-bound programs with clear milestones and decision points.
Run delivery end-to-end without needing a playbook handed to you. You write the playbook.
Hold engineering, product, and business teams accountable to commitments — without relying on hierarchy to do it.
Understand how Data Platform programs create upstream capability for AI and ML — design with that lens from day one.
Own AI-specific program tracks: NLQ rollout, Bedrock integrations, model deployment pipelines, and data readiness for AI.
Partner with Data Engineering, Product, and Science teams to ensure programs are not just delivered but compound into AI readiness.
Push teams to think end-to-end: what does this program unlock downstream, and are we building toward that?
Represent the AI program portfolio to senior leadership with clarity on status, risks, and strategic trajectory.
Own delivery of modernized integration programs — ensuring cross-application flows (ERP, CRM, supply chain) work reliably on a modern tech stack.
Drive the retirement of fragile legacy middleware and the adoption of event-driven, scalable integration patterns.
Partner with Finance, Operations, and Engineering to translate business process requirements into well-architected integration programs.
Ensure tax, order management, procurement, and revenue-impacting workflows have zero-tolerance reliability standards.
Define the integration roadmap in partnership with architects and engineering leads — own it, defend it, and ship it.
Operate effectively in a flat org: influence without hierarchy, build alignment without bureaucracy.
Be the connective tissue across Engineering, Product, Finance, and Operations — translate technical complexity into business impact.
Identify cross-program risks before they become incidents. Escalate with a proposed solution, not just a flag.