Forbes Advisor is a new initiative for consumers under the Forbes Marketplace umbrella that provides journalist- and expert-written insights, news and reviews on all things personal finance, health, business, and everyday life decisions. We do this by providing consumers with the knowledge and research they need to make informed decisions they can feel confident in, so they can get back to doing the things they care about most.
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
- 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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