Data Operations Program Leader — End-to-End Pipelines

Deepgram; Northern (KY); Hybrid; USD 90,000 - 120,000; Full time

Job summary

Deepgram is seeking a hands-on Program Manager to lead data programs across our voice AI platform. You will build data pipelines, curate labeled datasets, and align data strategy with model training goals.

The role requires collaboration with Research, Engineering, and Product teams, ensuring scalable processes and high-quality outputs. You’ll shape data workflows, manage data vendors, and drive continuous improvements in speed and cost-efficiency, enabling production-ready datasets for

Qualifications

• Experience owning data, ML, or ops programs end-to-end in PM or product roles.
• Fluent in collaborating with technical teams on data quality and model impact.
• Ability to design scalable processes across teams and data pipelines.
• Strong prioritization under constraints and clear decision-making.

Responsibilities

• Design, launch, and own end-to-end data workflows from raw data to production-ready datasets.
• Develop labeling specs, style guides, and documentation for global annotation teams.
• Identify tooling improvements and lead automation and workflow optimization.
• Translate product goals into data creation strategies aligned with model training.
• Own domain lifecycle including data acquisition, preparation, scaling, provenance, and evaluation.
• Prototype and deploy data tools and infrastructure; collaborate with Research/Engineering.
• Track data quality, throughput, and vendor performance; manage external contributors.

Skills

Data PM

Systems thinking

Prioritization

Cross-functional collaboration

Job description

Deepgram is seeking a hands-on Program Manager to lead data programs across our voice AI platform. You will build data pipelines, curate labeled datasets, and align data strategy with model training goals.

The role requires collaboration with Research, Engineering, and Product teams, ensuring scalable processes and high-quality outputs. You’ll shape data workflows, manage data vendors, and drive continuous improvements in speed and cost-efficiency, enabling production-ready datasets for