Technical Product Manager, Data and Internal Products
New
J
JobgetherIdentity and fraud prevention
Based in the United StatesFull-TimeMiddle
SalaryAnnual base salary: $180,000–$230,000 USD.
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Job Details
- Experience
- 5+ years of experience in product management, founding roles, or equivalent cross-functional product leadership positions.
- Required Skills
- AWSPythonProduct ManagementSnowflakeSparkBigQueryDatabricks
Requirements
- Have 5+ years of experience in product management, founding roles, or equivalent cross-functional product leadership positions.
- Have experience managing big data products and internal tools, with specific projects identified on your resume.
- Have at least one year of experience at a Series B or Series C startup.
- Be currently or most recently employed at a startup rather than a large technology company.
- Have previous experience in a technical role such as Software Engineer, Data Scientist, Machine Learning Engineer, or highly technical Data Analyst.
- Have production-level experience with data pipelines, including data ingestion, cleaning, normalization, and modeling.
- Be familiar with Python and AWS.
- Have hands-on experience with at least one major big data technology: Spark, Databricks, BigQuery, Snowflake, or Redshift.
- Have a strong understanding of big data concepts, data architecture, data pipelines, and data modeling.
- Have experience collaborating with engineering, data science, and analytics teams on complex technical challenges.
- Have experience leading large internal initiatives, coordinating stakeholders, and managing cross-functional dependencies.
- Fintech, payments, fraud prevention, or identity verification experience is advantageous.
Responsibilities
- Own the product vision, roadmap, and execution for internal technology initiatives involving data-intensive systems and infrastructure.
- Lead cross-functional projects to improve the quality, reliability, accessibility, and usability of internal datasets, platforms, and shared services.
- Drive initiatives to modernize internal data and technology assets so they can scale with business and technical requirements.
- Partner with engineering, data science, solutions analytics, and product teams to define use cases and translate technical challenges into product requirements.
- Prioritize competing internal demands, balancing immediate operational impact with long-term strategic value.
- Coordinate complex initiatives across stakeholders, clarifying ownership, alignment, and cross-functional dependencies.
- Support development and improvement of data pipelines, data models, and internal tooling for analytics and machine learning workflows.
- Monitor product performance and identify opportunities to improve internal systems and technical processes.
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