Lead Technical Program Manager, Data

Wayve•Sunnyvale, CA

2d•$259,200 - $312,400•Hybrid

About The Position

As the Lead Technical Program Manager for Data at Wayve, you will build and lead the technical program management function for the Data organization. This includes the data platform, ingestion, standardization pipelines, and the enrichment, curation, and labeling that fuel the AI Driver. The data corpus is also critical for teams working on simulation and validation. You will act as the delivery partner to engineering leadership for data teams and drive the department-wide objective to “make valuable data immediately and reliably usable”. You will lead flagship programs directly while supporting and coaching a small, high-impact TPM team. Your impact will be measured in data quality, throughput, cost, and the predictability of data landing for model training. A successful TPM leader is a force multiplier, helping teams move faster, more effectively, and with purpose.

Requirements

8+ years in data: Deep, hands-on experience delivering data programs as a technical program manager - data platform, pipelines, enrichment / curation, labelling - including people and process leadership.
You've built or scaled a program function and you get things done with ownership and a bias for action.
People leadership: You build, coach, and grow high-performing teams, and raise the bar on the craft.
Technically strong: Credible with engineers on data platforms, pipelines, and large-scale data infrastructure, even though you won't write production code.
Experience with distributed data platforms at Petabyte scale.
Experience with data ingestion from raw data to processing through different stages and pipelines.
Understands Directed Acyclic Graphs, their usage and challenges.
Solid understanding of Machine Learning, GPUs and training and inference of 500M-20B parameter models.
Proficient with some or all of Databricks, Flyte, Ray, Kubernetes, Azure, Docker, Python.
Proficient with AI Agents to accelerate execution: eg. Cursor, Claude, Codex.
Stays neutral and adapts under pressure: Effective in ambiguous, fast-moving environments; you flex your style and bring structure without slowing delivery.
Systems thinking: You reason about how the parts of a complex data system fit together, and how a change in one area ripples into others.
Product-minded: You focus on outcomes and the people your programs serve - prioritizing by impact and defining what good looks like, not just tracking activity.
Exceptional cross-functional leadership: You align and influence across data, ML, and engineering without relying on authority.
Strategic & business impact: You link data investment to measurable outcomes - quality, throughput, cost.
Clear, data-driven communicator: You set direction with clarity and steer with the right metrics.
Growth mindset: Open to feedback and always looking to improve.

Nice To Haves

Working experience with a large scale distributed data platform used by 100s of engineers and researchers.
Background in autonomous vehicles, robotics, or another large-scale data / ML program.
An engineering or computer science degree, or experience working as an engineer; fluent with modern delivery tooling (e.g. Jira).

Responsibilities

Lead the function: Build, coach, and grow a small TPM team; hire to fill gaps and raise the bar.
Own data delivery: Partner with Data leadership on planning, prioritization, and execution across the data roadmap.
Trusted partner for Engineering: Maintain the partnership with Engineering leaders to land big outcomes, pushing teams to land impact with high leverage and hold leaders to account.
Drive flagship programs directly: Data platform, data pipelines, enrichment & curation, labelling / annotation, and dataset management and quality.
Establish scalable practices: Planning cadences, governance, KPIs and dashboards, escalations and operational reviews.
Align cross-functionally: Work with data, ML, engineering, and external data / annotation suppliers; manage dependencies, risk, and trade-offs.
Connect to outcomes and represent the data org: Tie delivery to data quality, throughput, cost, and time-to-insight, and represent the data org in company-level reviews.