Zoop.One - Product Manager - Data Sourcing & Engineering

About the Role : ZOOP Analytics is building a data-driven intelligence platform across Observability, Diagnostics, and Forecasting. This role owns the product management of the data foundation behind these solutions - from dataset activation and ingestion to data quality, resolution, joins, and refresh reliability - translating platform needs into a clear engineering roadmap. Key Responsibilities : Data Sourcing : - Own and prioritize the dataset activation roadmap across vehicle, credit, and third-party data sources. - Translate product requirements into clear dataset and data-layer requirements. - Drive datasets from POC - In Progress - Live, with defined milestones and decision points. - Coordinate with data vendors on onboarding, access, and licensing. Data Engineering : - Own the backlog for ingestion, standardisation, entity resolution, deduplication, joins, and aggregation. - Define requirements for core data pipelines and monitor refresh SLAs and quality. - Establish data-quality checks and drive resolution of pipeline or refresh issues with engineering. Governance & Delivery : - Partner with Legal, Compliance, and Security on data privacy, usage, retention, and regulatory requirements. - Work with product teams across both verticals to prioritize platform-layer requirements. - Maintain documentation and support sprint planning, backlog grooming, and delivery tracking. What Success Looks Like : - Reliability : >=98% of scheduled refreshes meet defined SLAs. - Specification quality : <10% of requirements require rework due to ambiguity. - Roadmap discipline : Dataset priorities are regularly reviewed and linked to product needs. - POC conversion : Active POCs have clear, time-bound decisions to progress or deprioritize. Requirements : - 4-5 years in Product Management or Technical Program Management in a data-heavy environment. - Strong understanding of data pipelines, ETL/ELT, data modelling, entity resolution, joins, and data quality. - Hands-on SQL skills for validation, debugging, and data analysis. - Familiarity with APIs, data contracts, orchestration, SLAs, and data-quality frameworks. - Experience with a cloud data warehouse such as BigQuery, Snowflake, or Redshift. - Comfortable using BI tools such as Metabase, Looker, or Power BI. - Basic Git knowledge and strong requirement-writing and prioritisation skills. - Strong communication across product, engineering, vendors, and business teams. - Bachelor's degree in Engineering, Computer Science, Statistics, or a related quantitative field. Good to Have : - Automotive or vehicle-data domain experience. - BFSI/NBFC data exposure. - Experience with data vendors, licensing, or procurement. - Basic Python or scripting experience for data validation.