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 ResponsibilitiesManage initiatives to improve data product offering work with engineering or use AI tools to define problems, shape solutions, and shipInvestigate data quality issues independently and own the full feedback loop, from root cause to resolution to clear communication with Customer Success and customers (joining customer calls, providing written resolution notes, etc)Design QA procedures to proactively identify and prevent data integrity issuesBuild repeatable, automated checks to reduce reliance on manual investigationRequirements2-4 years in a technical product management or analytics roleStrong expertise in data analysis (Python, SQL)Experience in QA for datasetsA bias for automating repetitive tasks and reproducibilityExperience using AI tooling to accelerate data analysis, investigation, and documentationProfessional-level English proficiency, as English is the primary working language across the company (documentation, Slack, meetings)Nice to HaveExperience owning or contributing to AI/ML-adjacent product initiativesExperience with large geospatial datasetsExperience communicating directly with customersWhat Success Looks LikeRobust QA processes set upAI product initiatives shipping without needing close managementData quality issues resolve faster, with fewer escalations to engineering and clearcommunication to CS and customers
