Senior Data Engineer

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 Meta 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.

This role goes beyond building pipelines - will be responsible for:

• Designing scalable data architecture
• Driving business outcomes (revenue, lead quality, conversion efficiency)
• Owning how data is used, trusted and acted upon

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:

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

4. Data Quality & Governance

• Implement validation checks, monitoring and alerting for 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

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

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)