Lead Data Engineer at ForbesAdvisor

Responsibilities:  1. 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 2. Marketing & Ad Platform Data 1.    Own ingestion and modeling of data from Meta Ads (Facebook) and other digital marketing platforms 2.    Build datasets that support: ·    Campaign performance tracking ·    Lead funnel analysis ·    Attribution and conversion tracking 3.    Understand key concepts such as: ·    Campaign structure (campaign/ad set/ad level) ·    Bidding & optimization signals ·    Attribution windows ·    Pixel / event tracking 3. 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 4. Data Quality & Governance ·    Implement validation checks, monitoring and alerting for pipelines ·    Ensure consistency across different marketing data sources ·    Maintain clear documentation of data models and pipelines 5. Business Collaboration & Use Case Ownership •    Work closely with marketing, growth, and analytics teams to:     Understand real-world use cases     Define success metrics tied to revenue and performance •    Own key use cases such as:     Lead funnel optimization     Campaign attribution     Revenue reporting and forecasting •    Ensure data enables decision-making, not just reporting 6. 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     Documentation and data contracts ·    Contribute to shared frameworks and reusable components ·    Promote best practices across the data engineering team