Lead Data Engineer

ravio; United Kingdom; On-site; GBP 90,000 - 120,000; Full time

Job summary

Ravio is seeking a Lead Data Engineer to lead a small team of data engineers within our Engineering team. You'll remain hands-on designing, building, and operating the data systems behind our compensation benchmarks, customer-facing tools, and AI agents.

You’ll combine technical leadership with line management and coaching, working with engineering and product colleagues to turn priorities into delivered outcomes.

Qualifications

• Six-plus years' experience in data engineering and at least two years in leadership.
• Strong Python and SQL skills with production data systems experience.
• Experience with data modelling, dbt, and orchestration workflows.

Responsibilities

• Lead the data engineering team to design, build, and operate Ravio's data pipelines and services.
• Shape and deliver the data roadmap with Product, Data Science, and Insights.
• Ensure data quality, governance, and security in daily delivery.
• Own production data systems reliability and day-to-day support.
• Use AI-assisted tools for development, testing, documentation, and investigation.

Skills

Python

SQL

Data engineering

Leadership

Tools

dbt

CI/CD

Job description

We help companies get compensation right. What we get paid at work has a massive impact on our lives, and it's one of the biggest factors in hiring and retaining talent - and yet so many companies struggle to get it right. They simply have no choice but to rely on poor data and unsophisticated tools for their pay decisions. That's why we created Ravio.

We help many of the world's most innovative and ambitious companies build stronger teams and reach their goals through better compensation. Our real-time data platform brings compensation into the modern age, tracking the market across all compensation types from salary to equity and benefits. We're passionate about ensuring everyone is paid what they deserve, no matter their background or circumstances. We believe that when compensation is done right, everyone wins.

Ravio is growing fast, which means you can too. We've established ourselves as the European leader in our space serving more than 1,200 clients, and now have our sights set on becoming the global go to place for compensation data and tools for managing compensation.

Joining a startup and scaling it into a global product is one of the most challenging and rewarding experiences a career can offer. If that sounds exciting to you, you're in the right place.

The role

We're looking for a Lead Data Engineer to lead a small team of data engineers within our Engineering team. You'll remain hands-on in designing, building, and operating the data systems behind our compensation benchmarks, customer-facing tools, and AI agents. You'll take a platform engineering approach to data, building reliable, reusable capabilities that help Engineering, Data Science, and Insights deliver. You'll combine technical leadership with line management and coaching, working with engineering and product colleagues to turn priorities into delivered outcomes.

You’ll report to our VP Engineering.

Hands-on engineering and delivery

Be a senior technical contributor, building and operating the pipelines, integrations, and data services behind Ravio's product. Design clear interfaces between ingestion, transformation, and downstream applications so teams can build on trusted data without bespoke support for every change.

Shape and deliver the data roadmap with Product, Data Science, and Insights. Prioritise improvements that deliver customer value, reduce recurring manual work, or address reliability needs, while keeping complexity and cloud costs under control.

Build data quality, governance, and security into everyday delivery through automated checks, clear data ownership, and appropriate access controls.

Work with engineering colleagues to integrate data systems into shared development, deployment, and monitoring practices. Use version-controlled transformations, automated tests, and repeatable deployments, reducing bespoke tooling where it adds unnecessary overhead.

Own the reliability and day-to-day support of the team's production data systems, improving monitoring and recovery. Build reusable ingestion patterns and self-service workflows around recurring needs, measuring their value through faster delivery, fewer failures, and less manual support.

Use AI-assisted tools for development, testing, documentation, and investigation. Help the team use them effectively, with appropriate review of outputs and protection of customer data.

Team management and leadership

Lead the team's delivery planning and ways of working, keeping priorities, scope, and dependencies visible. Take part in shared product prioritisation, bringing requests from Data Science, Insights, and Operations into an agreed delivery plan.

Partner with software engineers, product managers, and data colleagues to make timely decisions and share responsibility for delivered outcomes.

Line manage and coach a small team of data engineers, providing regular feedback and supporting their technical and career development.

Build a supportive, accountable team with shared ownership of its systems. Use pairing, code reviews, documentation, and operational handovers to spread knowledge.

What we're looking for

• Six-plus years' experience in data engineering, with at least two years in a team leadership role.
• Strong Python and SQL skills, with experience building and operating production data systems.
• Experience with data modelling, transformation frameworks such as dbt, and workflow orchestration.
• Experience applying platform engineering principles to data systems, such as reusable ingestion patterns, clear data interfaces, or self-service tooling that other teams use successfully.
• Experience with cloud infrastructure, infrastructure as code, automated testing, and continuous integration and delivery (CI/CD).
• Practical understanding of reliable data processing, including schema changes, retries, safe reprocessing, and backfills.
• Experience using monitoring and data quality checks to investigate failures and improve reliability.
• Practical use of AI-assisted engi