SentiLink
Technical Product Manager, Data and Internal Products
United States
Sponsorship not specifiedDetected 161 days ago
BigQuerySnowflakeDatabricksMachine LearningSparkData EngineeringData ScienceProduct StrategyProject ManagementLeadershipCommunicationCollaboration
About the role
- SentiLink https://www.sentilink.com/ provides innovative identity and risk solutions, empowering institutions and individuals to transact with confidence.
- We've seen tremendous traction and are growing extremely quickly.
- SentiLink is backed by world-class investors including Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin.
Responsibilities
- Lead cross-functional efforts to improve the quality, reliability, and usability of internal systems, including datasets, shared services, and internal platforms.
- Drive long-term initiatives to future-proof internal data and technology assets, ensuring they scale with evolving product, analytics, and operational needs.
- Partner with engineering, data science, solutions analytics, and product stakeholders to understand use cases, clarify requirements, and identify interdependencies.
- Manage prioritization across competing internal demands, balancing immediate impact with long-term strategic value.
Requirements
- Strong understanding of big data concepts, data pipelines, data cleaning/normalization, and data modeling.
- Experience driving large internal initiatives with multiple stakeholders and managing substantial cross-functional dependencies.
- Excellent communication skills, with the ability to explain technical concepts to diverse audiences and facilitate alignment across teams.
Nice to have
- 5+ years of experience with data-heavy or platform-oriented products, with a focus on ML/Data Science strongly preferred.
Compensation
- $180,000/year - $230,000/year + equity + benefits
Benefits
- Employer paid group health insurance for you and your dependents
- Flexible paid time off
- Home office stipend, and more!
- Own the product vision, roadmap, and execution for internal technical initiatives, with a strong focus on data‐intensive systems and foundational datasets.
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