Data Engineer L3

Forbes Advisor

United States

On-site

USD 120,000 - 170,000

Full time

14 days+

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Benefits offered by this job

Day off on the 3rd Friday of every月

Monthly Wellness Reimbursement Program

Monthly Office Commutation Reimburse"

Paid paternity and maternity leaves

Job summary

Forbes Advisor is seeking a highly skilled Data Engineer (L3) with strong Python, data ingestion pipelines, and marketing data systems expertise, especially with Meta Ads ecosystem. This role blends data engineering with social/native platforms to enable scalable pipelines and high-quality datasets for lead generation and business decisions.

The ideal candidate will design scalable data architecture, own data quality and governance, and collaborate with data and engineering leads to deliver

Qualifications

Strong Python and SQL skills for large-scale data processing.
Experience with API data ingestion and rate limits.
Proficiency with Airflow or similar orchestration tools.

Responsibilities

Design, build, and maintain scalable data pipelines for social marketing and product data sources.
Develop ETL/ELT workflows using Python and SQL.
Ensure data quality, reliability, and observability across pipelines.
Own ingestion and modeling of data from Meta Ads and other platforms.
Translate business requirements into scalable data solutions.
Define success metrics tied to revenue and performance.
Enable self-serve analytics with well-structured datasets.
Design modular, reusable microservices and promote best practices.

Skills

Python

SQL

APIs ingestion

Airflow

Performance optimization

Microservices

Data modeling

Big data concepts

Tools

Airflow

BigQuery

dbt

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.

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

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 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