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 transparent 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 Required Skills & Qualifications 1. Core Technical Skills • • • • • • • • Strong proficiency in Python (must-have) Advanced SQL skills for large-scale data processing Hands-on experience with data ingestion from APIs (rate limits, pagination, retries) Experience with data orchestration tools (e.g., Airflow or equivalent) Familiarity with cloud data platforms (BigQuery, etc.) Experience building scalable data ingestion systems Familiarity with microservices-style or modular data systems Strong understanding of performance and cost optimization 2. Ad Platform Knowledge • • • • Solid understanding of Meta Ads platform fundamentals Familiarity with: • Campaign hierarchy and metrics (CTR, CPC, CPA, ROAS) • Conversion tracking and attribution models • Lead generation workflows and funnel metrics Ability to interpret marketing data beyond surface-level metrics Exposure to event tracking systems (GA4, Snowplow, etc)

📌 Data Engineer (Chennai)
🏢 NXT Door Retail
📍 Chennai