Partner One Capital
Data as a Service (DaaS)
DataProductManager
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Product Manager at Partner One Capital. Skills: Data Product Management, AI Product Initiatives, Data Quality. Manage initiatives to improve data product offering. Work with engineering or use AI tools to define problems, shape solutions, and ship”
What You'll Achieve.
Robust QA processes running in production; AI product initiatives shipping without needing close management; Data quality issues resolve faster, with fewer escalations to engineering and clear communication to CS and customers
Industry & Context.
Identify data integrity risks early; Accelerate investigations; Root cause to resolution
What They're Looking For.
Must Have
2-4 years in a technical product management or analytics role at a data-heavy company, technical expertise in working with dataframes and complex data manipulation, Experience in building and implementing QA for datasets, A bias for automating repetitive tasks and reproducibility, Experience using AI tooling to accelerate data analysis, investigation, and documentation, Professional-level English proficiency
Nice to Have
Experience owning or contributing to AI/ML-adjacent product initiatives, Experience with large geospatial datasets, Experience with Pyspark, Scala, and Apache Sedona, Experience communicating directly with customers
What You'll Do.
Manage initiatives to improve data product offering
Work with engineering or use AI tools to define problems
Investigate data quality issues independently
Own the full feedback loop
from root cause to resolution to clear communication with Customer Success and customers
and implement QA procedures to proactively identify and prevent data integrity issues
automated checks to reduce reliance on manual investigation
How You'll Work.
Team & Collaboration
Work closely with Customer Success, Sales, and Engineering to identify data integrity risks early, accelerate investigations, and ship better data
Communication Scope
Clear communication with Customer Success and customers; Clear communication to CS and customers
Full Job Description
SafeGraph is a Data as a Service (DaaS) company with one focus: curating the most accurate, precise, and fresh points of interest (POI) database on the planet. We provide product builders, data scientists, and analytics teams with the location data they need to power site selection, transaction enrichment, advertising audiences, competitive intelligence, and more. Our customers include companies like Plaid, Mapbox, Clear Channel — spanning fintech, retail, real estate, adtech, logistics, and government. We’re fully remote, lean by design, and serious about data quality. You will play a critical role in shaping how our data products are built, validated, and trusted. This role combines product thinking with hands-on technical execution - you'll drive AI-powered product initiatives while owning the data quality processes our customers depend on. You'll sit on our Product team and work closely with Customer Success, Sales, and Engineering to identify data integrity risks early, accelerate investigations, and ship better data. ### Key Responsibilities * Manage initiatives to improve data product offering work with engineering or use AI tools to define problems, shape solutions, and ship * Investigate data quality issues independently and own the full feedback loop, from root cause to resolution to clear communication with Customer Success and customers * Design, develop, and implement QA procedures to proactively identify and prevent data integrity issues * Build repeatable, automated checks to reduce reliance on manual investigation **Requirements** * 2-4 years in a technical product management or analytics role at a data-heavy company * Strong technical expertise in working with dataframes and complex data manipulation (Python, SQL, Pandas, Spark) * Experience in building and implementing QA for datasets * A bias for automating repetitive tasks and reproducibility * Experience using AI tooling to accelerate data analysis, investigation, and documentation * Professiona
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