Lead Data Engineer

Lead Data Engineer

Hybrid (London /Reading - 1-2 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? We're partnering with a global leader in marketing data and insights that's 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: 1-2 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 you'll 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 we're 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.