Lead Data Engineer

About the Role

As a Lead Analytics Engineer, you will be responsible for designing, building, and continuously improving trusted, scalable data products that enable analytics, reporting, machine learning, and business decision-making across the organisation. Acting as a technical leader within the data team, you will translate complex business requirements into well-governed data models, feature marts, and data pipelines while driving best practices in analytics engineering. You will collaborate closely with Data Engineers, Data Scientists, Analysts, Product Teams, and business stakeholders to ensure the delivery of reliable, production-ready data solutions that create measurable business value. This role combines deep technical expertise with leadership responsibilities, including mentoring team members, establishing engineering standards, improving data quality, and driving the adoption of modern analytics engineering practices.

What You'll Do

• Analytics Engineering & Data Product Development
• Lead the design, build, and optimisation of analytics-ready data models, data marts, feature marts, and data pipelines.
• Translate complex business requirements into scalable, reusable analytics engineering solutions.
• Develop trusted data products that support analysts, data scientists, machine learning teams, and business users.
• Design and maintain robust data models that enable high-impact analytics use cases across the organisation.
• Technical Leadership
• Provide technical leadership throughout the analytics engineering lifecycle, including solution design, data modelling, development, testing, deployment, and monitoring.
• Lead code reviews, data model reviews, and solution design discussions.
• Establish and drive analytics engineering standards, best practices, and governance frameworks.
• Guide and mentor Analytics Engineers and broader data practitioners.
• Promote software engineering best practices, reusable design patterns, and documentation standards.
• Data Quality, Governance & Reliability
• Implement and maintain data quality controls, automated testing, monitoring, and quality assurance procedures.
• Ensure data products are accurate, reliable, scalable, and production-ready.
• Maintain clear documentation, data definitions, lineage, and business rules.
• Drive governance and consistency across data products and analytical solutions.
• Stakeholder Collaboration
• Partner with Data Engineers, Data Scientists, Analysts, and business stakeholders to refine requirements and deliver data-driven solutions.
• Improve data availability and resolve data quality issues at the source.
• Communicate technical solutions and recommendations to both technical and non-technical audiences.
• Build strong relationships across business and technology teams.
• Operational Support & Continuous Improvement
• Investigate and resolve production incidents, pipeline failures, and data-related issues.
• Diagnose root causes and implement sustainable fixes and preventative controls.
• Identify opportunities to improve tooling, automation, governance, and engineering processes.
• Drive the continuous evolution of analytics engineering capabilities and delivery standards.

What You Bring

• Qualifications
• Degree or Diploma in: Computer Science, Data Engineering, Information Systems, Mathematics, Statistics, Engineering, or a related field.
• Equivalent practical experience in Analytics Engineering, Data Engineering, or Data Product Development may be considered.
• Experience
• 6+ years' experience within Data Engineering, Analytics Engineering, Data Science, or related data environments.
• Proven experience designing, building, and productionising data models, pipelines, architectures, and analytics-ready datasets.
• Experience providing technical leadership, coaching, code reviews, and engineering guidance within data teams.
• Experience working with cross-functional technical and business stakeholders.
• Experience operating within Agile delivery environments.
• Exposure to Retail, Ecommerce, Financial Services, or other large-scale digital businesses is advantageous.
• Technical Skills
• Advanced SQL development, optimisation, and performance tuning.
• Strong data modelling expertise, including: dimensional modelling, data transformation, business rules implementation, data quality controls.
• Experience building scalable analytics data products and data pipelines.
• Knowledge of data governance, metadata management, and lineage principles.
• Strong understanding of testing, deployment, monitoring, and production support practices.
• Experience with modern cloud-based data platforms.
• Proficiency in Python, PySpark, or similar data processing technologies (advantageous).
• Experience with version control and software engineering practices.
• Leadership & Behavioural Competencies
• Strong technical leadership and mentoring capabilities.
• Excellent analytical and problem-solving skills.
• Ability to solve complex data challenges using structured thinking and sound technical judgement.
• Quality and governance-focused mindset with exceptional attention to detail.
• Strong stakeholder management and relationship-building skills.
• Ability to communicate complex technical concepts clearly and effectively.
• Delivery-focused with strong ownership and accountability.
• Curious, innovative, and continuously seeking better ways of working.
• Resilient and adaptable in fast-paced environments.
• Passionate about improving data capabilities and enabling data-driven decision making.