The Role
We’re hiring a Data Engineering Lead to help scale and guide a growing team of data engineers. This role is ideal for someone who enjoys solving technical challenges hands-on while also shaping engineering best practices, coaching others, and helping cross-functional teams deliver data products with clarity and speed.
You’ll manage a small team of ICs responsible for building and maintaining pipelines that support reporting, analytics, and machine learning use cases. You’ll be expected to drive engineering excellence — from code quality to deployment hygiene — and play a key role in sprint planning, architectural discussions, and stakeholder collaboration.
This is a critical leadership role as our data organization expands to meet growing demand across media performance, optimization, customer insights, and advanced analytics.
What you’ll do
Lead and grow a team of data engineers working across ETL/ELT, data warehousing, and ML-enablement
Own team delivery across sprints, including planning, prioritization, QA, and stakeholder communication
Set and enforce strong engineering practices around code reviews, testing, observability, and documentation
Collaborate cross-functionally with Analytics, BI, Revenue Operations, and business stakeholders in Marketing and Sales
Guide technical architecture decisions for our pipelines on GCP (BigQuery, GCS, Composer)
Model and transform data using dbt and SQL, supporting reporting, attribution, and optimization needs
Ensure data security, compliance, and scalability — especially around first-party customer data
Mentor junior engineers through code reviews, pairing, and technical roadmap discussions
What You Bring
6+ years of experience in data engineering, including 2+ years of people management or formal team leadership
Strong technical background with Python, Spark, Kafka, and orchestration tools like Airflow
Deep experience working in GCP, especially BigQuery, GCS, and Composer
Strong SQL skills and familiarity with DBT for modeling and documentation
Clear understanding of data privacy and governance, including how to safely manage and segment first-party data
Experience working in agile environments, including sprint planning and ticket scoping
Excellent communication skills and proven ability to work cross-functionally across global teams.
Nice to have
Experience leading data engineering teams in digital media or performance marketing environments
Familiarity with data from Google Ads, Meta, TikTok, Taboola, Outbrain, and Google Analytics (GA4)
Exposure to BI tools like Tableau or Looker
Experience collaborating with data scientists on ML workflows and experimentation platforms
Knowledge of data contracts, schema versioning, or platform ownership patterns
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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