Data Engineer

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

3. 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

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)

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