Lead Data Engineer

Lead Data Engineer

Hybrid (London /Reading 12 days per week)

Up to 90,000 + Benefits

Are you ready to take the lead in shaping world-class data infrastructure at one of the most influential analytics companies on the planet? Were partnering with a global leader in marketing data and insights thats seeking a Lead Data Engineer to help scale and evolve its cloud-based data ecosystem.

This is an opportunity to work on high-impact global datasets , enabling better analytics, stronger fraud prevention, and smarter decision-making across 90+ markets.

Why this role?

• Play a key role in building and optimising global data pipelines supporting over 50 consumer panels worldwide.
• Lead the technical strategy for data architecture, quality, and scalability.
• Collaborate closely with data scientists, analysts, and software engineers to drive innovation in data engineering.
• Hybrid flexibility: 12 days per week in London, Reading, or Southbank office , balance remotely.
• Join a global organisation with the resources of a world leader and the agility of a fast-moving data tech team.

What youll be doing:

• Leading the design, development, and maintenance of data pipelines and infrastructure across AWS and Azure.
• Building and evolving systems that detect and prevent fraud and data anomalies in large-scale survey data.
• Integrating diverse data sources (APIs, databases, external datasets) into a unified analytics ecosystem .
• Automating data ingestion and transformation workflows using modern ELT/ETL best practices.
• Implementing monitoring and alerting systems to ensure high data quality and reliability.
• Mentoring a small team of data engineers, driving excellence and continuous learning.
• Partnering with Data Science and BI teams to deliver reliable, low-latency data for analytics and reporting.

What were looking for:

• 5+ years experience in data engineering, including technical or team leadership.
• Expertise in AWS (preferred) or Azure , and experience managing data pipelines in the cloud.
• Strong skills in Python and SQL , with a solid grasp of data warehousing concepts.
• Experience with DBT , Redshift , or Postgres , and familiarity with BI tools (Power BI preferred).
• Proven understanding of CI/CD principles , DevOps, and infrastructure automation.
• Excellent communication skills able to collaborate across technical and business functions.
• A passion for data-driven problem solving, innovation, and continuous improvement.

Nice-to-haves:

• Experience with data orchestration tools (Airflow, Prefect).
• Knowledge of data observability or cataloguing tools (Monte Carlo, OpenMetadata).
• Familiarity with large-scale consumer data or survey environments.