Data Engineer L3

The core responsibilities for the job include the following:

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 (Facebook) and other digital marketing platforms.
• Build datasets that support campaign performance tracking, lead funnel analysis, and 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 and campaign attribution.
• 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 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. ).

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.