Responsibilities:
Data Engineering & Pipelines
Design, build, and maintain robust data data pipelines for social marketing and product data sources (APIs, event streams, batch systems)
Develop scalable ETL/ELT workflows / microservices using Python and SQL
Ensure high data quality, reliability and observability across pipelines
Optimize data models for analytics and reporting use cases
Marketing & Ad Platform Data
Own ingestion and modeling of data from Meta Ads (Facebook) and other digital marketing platforms
Build datasets that support campaign performance tracking, lead funnel analysis, attribution and conversion tracking
Understand key concepts such as campaign structure (campaign/ad set/ad level), bidding & optimization signals, attribution windows, pixel / event tracking.
Business Understanding & Collaboration
Translate business requirements from marketing, growth and product teams into scalable data solutions
Define success metrics tied to revenue and performance
Enable self-serve analytics through well-structured datasets
Data Quality & Governance




Implement validation checks, monitoring and alerting for pipelines
Ensure consistency across different marketing data sources
Maintain explicit documentation of data models and pipelines
Business Collaboration & Use Case Ownership
Work closely with marketing, growth, and analytics teams to understand real-world use cases and define success metrics tied to revenue and performance
Own key use cases such as lead funnel optimization, campaign attribution and revenue reporting and forecasting
Ensure data enables decision-making, not just reporting
Engineering Standards & Best Practices
Design and implement modular, reusable microservices that enable the scalable development of data products.
Drive standardization through well-architected, loosely coupled services that can be leveraged across multiple use cases.
Uphold high standards in code quality and modularity, pipeline reliability and monitoring,

📌 Data Engineer Mumbai
🏢 Forbes Advisor
📍 Mumbai