Forbes Advisor seeks a Data Engineering Manager to 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 in building scalable, observable, and well-modeled data systems that power critical marketing, revenue, and analytics decisions. This role combines technical depth with delivery leadership to drive design, mentor engineers, and ensure high-quality releases.

What You'll Do

Lead, mentor, and develop a high-performing data engineering squad 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.
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).
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.
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.
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 We're Looking For

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.).

Technical Stack

SQL, Python
dbt, Airflow/Composer
Google Cloud Platform (GCP): BigQuery, Google Cloud Storage (GCS), Pub/Sub, Cloud Composer, Cloud Functions, Vertex AI

Team & Environment

You will lead a squad of 10–15 engineers with 3–4 direct reports and report to the Director of Data Engineering. The team is part of a remote-first, collaborative culture focused on shaping the engineering foundations of a world-class data organisation.