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AI Summary
Design and maintain scalable data pipelines and microservices to support marketing and product data ingestion. Collaborate with growth and analytics teams to define success metrics and enable self-serve analytics through high-quality, well-structured datasets.
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
- 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, attribution and conversion tracking
- 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 and 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
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
- 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 model, 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
What Success Looks Like
- Reliable, scalable pipelines for marketing data ingestion
- High-quality datasets enabling accurate campaign and lead analysis
- Strong partnership with marketing teams, translating business needs into data solutions
- Improved visibility into lead quality, attribution and campaign performance
- Clear ownership of end-to-end data use cases, not just components
Why Join Us
- Work at the intersection of data engineering and growth marketing
- Solve high-impact problems in performance marketing and attribution
- Own meaningful data products end-to-end
- Influence both technical architecture and business outcomes
- Be part of a team that values ownership, impact and engineering excellence
Perks:
- Day off on the 3rd Friday of every month (one long weekend each month)
- Monthly Wellness Reimbursement Program to promote health well-being
- Monthly Office Commutation Reimbursement Program
- Paid paternity and maternity leaves
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