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.