Forbes Advisor is looking for a lead editor with deep data engineering and automation expertise to help power our data-driven content. This role sits at the intersection of journalism, automation and data engineering—you’ll own the backend systems that make large-scale, trustworthy content possible.
This isn’t a traditional editor role. While strong editorial judgment and a journalist’s mindset are required, the core of the role is editorial automation, with data engineering as a key skill. You will build, maintain and scale the pipelines, automations and workflows that support content such as best-ofs, product reviews and rates coverage. You’ll operate within the Editorial Automation team, working closely with the Sr. Managing Editor to build content at scale. This role owns the automation layer that enables Forbes Advisor's data-driven journalism, from
data ingestion to publishing and monitoring.
Responsibilities:
Qualifications:
This is a senior, systems-owning role. Candidates should be comfortable operating as both an
automation lead and a technical editor.
Nice To Haves:
Vision & Impact:
Automation is a critical editorial infrastructure. This role ensures Forbes Advisor can publish
fast, accurate and transparent data-driven content—reliably, repeatedly and at scale.
The systems you build and maintain directly affect content accuracy, speed, scale and reader
trust. You’ll help ensure that data-driven journalism at scale is not just possible, but reliable,
transparent and sustainable.
Benefits:
Forbes Advisor 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.
#LI-REMOTE #LI-NM1
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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