Company 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.
We’re dedicated to helping turn aspirations into reality. We do this by providing consumers with the knowledge and research they need to make informed financial decisions they can feel confident in, so they can get back to doing the things they care about most.
Job Description
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
Why Join Us
● Monthly long weekends — every third Friday off
● Wellness reimbursement to support your health and balance
● Paid parental leave
● Remote-first culture with flexibility and trust
● Leadership role in a fast-growing global data team inside a globally recognized brand
Additional Information
Forbes Marketplace provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.