Forbes Advisor- Data Engineer L3

Data Engineer - L3

Job Description

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.

Company website: https://www.forbes.com/advisor...

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 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

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