The Role
As a Data Engineering Manager, you’ll lead a team of engineers responsible for the design, delivery, and reliability of our data platform.
You will guide a squad of 10–15 engineers (3–4 direct reports) in building scalable, observable, and well-modeled data systems that power marketing, revenue, and analytics decisions across the company.
This role combines technical depth with delivery leadership: you’ll drive design and best practices, mentor engineers, and partner with Program Management to ensure predictable, high-quality releases. You’ll collaborate across Analytics, RevOps, Data Ops, and Product to turn business problems into performant, future-ready engineering solutions.
What You’ll Do
Commercial Impact
You will be crucial to driving revenue growth for the business through flexible and scalable data ingestion, creation of microservices that can be used for decisioning and driving forward data practices.
Team Leadership and Delivery
Lead, mentor, and develop a high-performing data engineering squads delivering production-grade pipelines and services.
Set technical and operational standards for quality, documentation, and reliability.
Partner with Program Management to plan, prioritise, and track delivery against sprint goals.
Foster a culture of ownership, engineering excellence, and continuous improvement.
Architecture and Technical Design
Contribute to architectural reviews and solution design in collaboration with the Director of Data Engineering.
Ensure scalable, modular pipeline design and adherence to strong data modeling principles in dbt and BigQuery.
Lead the transition from monolithic pipelines to microservice-based data workflows that are reusable and observable.
Champion consistency and semantic alignment across datasets and layers (Bronze, Silver, Gold).
Platform Ownership
Oversee implementation and optimisation of workflows in GCP (BigQuery, Cloud Composer, Cloud Functions, Vertex AI etc.).
Drive observability, cost efficiency, and data quality through monitoring and alerting standards.
Lead adoption of best practices in version control, CI/CD, testing, and automated QA.
Evaluate new tools and frameworks that enhance scalability and engineering productivity.
Cross-Functional Collaboration
Translate stakeholder requirements into robust technical designs that align with marketing, analytics, and revenue goals.
Partner closely with Analytics, BI Ops, and RevOps to deliver reliable data foundations for attribution, 1PD activation, and performance optimisation.
Communicate progress, risks, and dependencies clearly to technical and non-technical partners.
Technical Standards and Mentorship
Review designs, pull requests, and pipelines for scalability, maintainability, and efficiency.
Coach engineers on clean coding practices, modular design, and testing discipline.
Embed best practices for documentation, schema governance, and dependency management.
Model engineering-first leadership — balancing technical depth with empathy, clarity, and decisiveness.
What You’ll Bring
Minimum Qualifications
10+ years of total experience in data or software engineering, including 2+ years leading teams.
Deep expertise in SQL, Python, and modern ELT tools (dbt, Airflow/Composer).
Strong understanding of data modeling, orchestration, and optimisation in GCP (BigQuery, GCS, Pub/Sub).
Demonstrated experience building or scaling data pipelines for marketing, attribution, or monetisation.
Proven ability to lead delivery within agile sprints and coordinate with cross-functional stakeholders.
Excellent communication, prioritisation, and leadership skills in distributed teams.
Nice to Have
Experience transitioning large-scale architectures to microservices or event-driven designs.
Familiarity with CI/CD pipelines, observability frameworks, and cost optimisation in cloud environments.
Exposure to machine-learning pipelines, model serving, or real-time scoring.
Background working with marketing or product analytics data sources (Google Ads, Meta, GA4, Taboola, etc.).
What Success Looks Like
Fully operational, microservice-based data workflows with strong observability and documentation.
High stakeholder trust and on-time delivery of revenue, attribution, and ingestion initiatives.
Consistent, semantic data models powering analytics and attribution across the business.
A self-sufficient, empowered engineering team operating with speed, clarity, and quality.
Data platform seen as a reliable, scalable foundation for 1PD strategy and future AI use cases.
Why Join Us
Remote-first, collaborative culture with flexible working hours.
Opportunity to shape the engineering foundations of a world-class data organisation.
Exposure to complex, high-impact problems across marketing, revenue, and product.
Monthly long weekends, wellness stipend, and generous parental leave.
Work with global leaders in data, marketing, and engineering — building systems that drive real business growth.
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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