Data Engineering Manager

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.