Data Engineer III

We are looking for a highly skilled Data Engineer (L3) with strong expertise in Python, data ingestion pipelines, and marketing data systems, particularly with the Ads ecosystem. This role sits at the intersection of data engineering and social/native platforms, enabling scalable data pipelines, high-quality datasets, and lead generation and business decision-making.
The ideal candidate will not manage campaign buying or bidding directly but must clearly. understand ad platform mechanics, attribution models, and lead quality scores and will work. closely with the Data Lead/Engineering Lead, acting as a key contributor in shaping. solutions, making technical decisions, and delivering high-impact data products.
Responsibilities:
Designing scalable data architecture.
Driving business outcomes (revenue, lead quality, conversion efficiency).
Owning how data is used, trusted, and acted upon.
Data Engineering and Pipelines:
Design, build, and maintain robust 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 and Ad Platform Data:
Own ingestion and modeling of data from Meta Ads/Google Ads/Any other Ads 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 and optimization signals, Attribution windows, and pixel/event tracking.
Business Understanding and 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 and Governance:
Implement validation checks, monitoring, and alerting for pipelines.
Ensure consistency across different marketing data sources.
Maintain clear documentation of data models and pipelines.
Business Collaboration and 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, and Revenue reporting and forecasting.
Ensure data enables decision-making, not just reporting.
Engineering Standards and 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, and Documentation and data contracts.
Contribute to shared frameworks and reusable components.
Promote best practices across the data engineering team.
Requirements:
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.
Ad Platform Knowledge.
Solid understanding of 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. ).
Good to Have:
Experience with other ad platforms (Google Ads, Bing Ads, etc. ).
Knowledge of data modeling best practices (e. g., star schema, dbt).
Experience with real-time or near-real-time data pipelines.

Experience
7-11 yrs