The Role
We are hiring a Senior Data Engineer to help design and scale the infrastructure behind our analytics, performance marketing, and experimentation platforms.
This role is ideal for someone who thrives on solving complex data problems, enjoys owning systems end-to-end, and wants to work closely with stakeholders across product, marketing, and analytics.
You’ll build reliable, scalable pipelines and models that support decision-making and automation at every level of the business.
What you’ll do
Build, maintain, and optimize data pipelines using Spark, Kafka, Airflow, and Python
Orchestrate workflows across GCP (GCS, BigQuery, Composer) and AWS-based systems
Model data using dbt, with an emphasis on quality, reuse, and documentation
Ingest, clean, and normalize data from third-party sources such as Google Ads, Meta, Taboola, Outbrain, and Google Analytics
Write high-performance SQL and support analytics and reporting teams in self-serve data access
Monitor and improve data quality, lineage, and governance across critical workflows
Collaborate with engineers, analysts, and business partners across the US, UK, and India
What You Bring
4+ years of data engineering experience, ideally in a global, distributed team
Strong Python development skills and experience
Expert in SQL for data transformation, analysis, and debugging
Deep knowledge of Airflow and orchestration best practices
Proficient in DBT (data modeling, testing, release workflows)
Experience with GCP (BigQuery, GCS, Composer); AWS familiarity is a plus
Strong grasp of data governance, observability, and privacy standards
Excellent written and verbal communication skills
Nice to have
Experience working with digital marketing and performance data, including:
Google Ads, Meta (Facebook), TikTok, Taboola, Outbrain, Google Analytics (GA4)
Familiarity with BI tools like Tableau or Looker
Exposure to attribution models, media mix modeling, or A/B testing infrastructure
Collaboration experience with data scientists or machine learning workflows
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
Forbes Advisor is looking for a Data Research Engineer - Data Extraction to join the Forbes Marketplace Performance Marketing team with a focus on supporting one of Forbes business verticals. If you're looking for challenges and opportunities similar to those of a start-up, with the benefits of an established, successful company read on.We are an experienced team of industry experts dedicated to helping readers make smart decisions and choose the right products with ease. Marketplace boasts decades of experience across dozens of geographies and teams, including Content, SEO, Business Intelligence, Finance, HR, Marketing, Production, Technology and Sales. The team brings rich industry knowledge to Marketplace’s global coverage of consumer credit, debt, health, home improvement, banking, investing, credit cards, small business, education, insurance, loans, real estate and travel.The Data Extraction Team is a brand new team who plays a crucial role in our organization by designing, implementing, and overseeing advanced web scraping frameworks. Their core function involves creating and refining tools and methodologies to efficiently gather precise and meaningful data from a diverse range of digital platforms. Additionally, this team is tasked with constructing robust data pipelines and implementing Extract, Transform, Load (ETL) processes. These processes are essential for seamlessly transferring the harvested data into our data storage systems, ensuring its ready availability for analysis and utilization.A typical day in the life of a Data Research Engineer will involve acquiring and integrating data from various sources, developing and maintaining data processing workflows, and ensuring data quality and reliability. They collaborate with the team to identify effective data acquisition strategies and develop Python scripts for data extraction, transformation, and loading processes. They also contribute to data validation, cleansing, and quality checks. The Data Research Engineer stays updated with emerging data engineering technologies and best practices.
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