Senior Technical Program Manager, Data
New
H
HyperscienceIntelligent document processing
[Remote] USFull-TimeSenior
Salary$122K - $206.5K; $122K – $206.5K • Offers Equity; Base Salary $122K – $206.5K • Offers Equity
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Job Details
- Experience
- 5 -8 years of technical program management experience
- Required Skills
- PythonSQLSnowflakedbt
Requirements
- Have 5–8 years of technical program management experience delivering complex, multi-team data programs in high-growth technology environments.
- Write SQL queries independently to explore schemas, validate datasets, and investigate anomalies.
- Have foundational knowledge of modern cloud data architectures, including tools such as Snowflake, dbt, or Fivetran.
- Use AI-assisted developer utilities such as Claude Code, Cursor, or GitHub Copilot for analytical workflows and technical problem-solving.
- Communicate technical trade-offs to executive leaders and software engineers while steering cross-functional alignment.
- Hold a bachelor’s degree in Computer Science, Information Systems, Engineering, or Statistics, or have equivalent practical experience.
- Product management exposure, including customer discovery, requirements documents, and multi-quarter roadmaps, is a bonus.
- Data engineering, analytics engineering, or business intelligence experience is a bonus.
- Python proficiency for exploratory analysis and lightweight automation is a bonus.
- Experience with regulatory compliance, data privacy standards, or enterprise data cataloging is a bonus.
- PMP, PMI-ACP, AWS Certified Data Analytics, or Snowflake SnowPro credentials are a bonus.
Responsibilities
- Lead enterprise data initiatives across product telemetry, platform health metrics, and modern analytics infrastructure from inception through organizational adoption.
- Coordinate with Engineering, Product, Sales, and Customer Operations to resolve technical dependencies and deliver programs.
- Author technical requirements, user stories, and execution roadmaps that translate business needs into milestones.
- Partner with engineering teams to improve pipeline reliability, data event streaming, and service level agreements.
- Establish data governance frameworks, unified data definitions, and requirements for automated anomaly detection.
- Implement intake processes and milestone tracking, conduct team meetings, and provide stakeholder updates to identify risks.
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