Lead Data Engineer

As a Lead Data Engineer, you will act as the technical authority within the data engineering team, responsible for defining how data solutions are designed and built across the organisation. You will own the day‑to‑day technical direction of the data platform, ensuring that pipelines, data models, and warehouse structures are scalable, maintainable, and aligned to best practice. Working closely with the Data Engineering Manager and the Head of Data Engineering & Governance, you will translate strategic direction into practical engineering approaches, while mentoring engineers and driving high technical standards across the team. This is a senior individual contributor role with significant influence, operating as a peer to the Data Engineering Manager, with a focus on technical leadership rather than people management.

Job Requirements

• Extensive experience in data engineering and data warehouse development
• Strong track record of designing and implementing scalable data pipelines
• Deep expertise in SQL and modern ELT tools (e.g. dbt or equivalent)
• Experience working with cloud data platforms (AWS preferred: Redshift, S3, Athena, etc.)
• Experience optimising data models for performance, scalability, and cost
• Proven experience leading technical design and code quality across a team
• Experience implementing CI/CD and version‑controlled workflows
• Familiarity with data quality, lineage, and monitoring practices
• Experience working closely with analytics, reporting, or data science teams
• Strong experience with Linux and or Unix command line including Bash/Shell scripting
• Experience with data pipeline orchestration and scheduling
• Knowledge of data governance, lineage, and quality monitoring practices.
• Knowledge and experience of Airflow, Fivetran and Google BigQuery

Academic Qualifications

• BSc Computer Science (or equivalent)

Technical Qualifications

• Strong SQL skills
• Talend Data Integration (or similar ETL platform)
• dbt Integration (or similar ETL platform)
• Linux and or Unix command line familiarity including shell scripting
• Certification in AWS data systems (or similar cloud platform) desirable
• Tableau Desktop/Server (or similar reporting system)

Job Responsibilities

Technical Ownership & Architecture

• Own the technical design and implementation approach for data pipelines, warehouse models, and transformations
• Define and evolve data modelling standards (e.g. star schema, incremental models, partitioning strategies)
• Ensure solutions are scalable, performant, and cost‑efficient across the data platform
• Review and approve technical designs and code across the team
• Act as the primary escalation point for complex technical challenges

Data Platform & Warehouse Ownership

• Act as the day‑to‑day technical owner of the data warehouse (BAU)
• Ensure consistency and maintainability across datasets, pipelines, and transformations
• Drive improvements in performance, reliability, and observability
• Lead technical input into the data warehouse rebuild and future evolution

Engineering Standards & Best Practice

• Define and enforce engineering standards, patterns, and reusable components
• Promote strong practices in:
  • version control
  • testing and validation
  • CI/CD for data pipelines
• Ensure high‑quality documentation of data models and pipelines
• Identify and address technical debt proactively

Data Quality & Reliability

• Lead implementation of data quality checks and monitoring frameworks
• Ensure pipelines are robust, observable, and alerting appropriately
• Drive improvements beyond basic checks (e.g. volumetric, anomaly detection, trend validation)
• Support root cause analysis and resolution of data incidents

Technical Leadership & Mentorship

• Provide hands‑on technical guidance to engineers and senior engineers
• Mentor team members on system design, coding standards, and problem‑solving
• Lead technical discussions, design reviews, and knowledge sharing
• Support capability uplift across the team

Collaboration & Translation

• Work closely with:
  • Analytics & Reporting
  • Data Science / ML
  • Platform Engineering
• Translate business and analytical requirements into scalable engineering solutions
• Partner with stakeholders to ensure solutions are fit for purpose and usable

Delivery Contribution

• Contribute to scoping and estimation of complex work
• Partner with the Data Engineering Manager to ensure:
  • technical feasibility
  • appropriate sequencing of work
• Focus on how work should be done, not managing delivery processes

Competencies

Data Architecture & Engineering – Designing scalable pipelines, modelling data flows, and building robust warehouse architectures.

At this level: Owns and defines how systems are built. Sets modelling standards, ensures consistency across pipelines, and makes pragmatic design decisions balancing speed, cost, and scalability.

Programming & Automation – SQL, dbt, Python, orchestration, CI/CD.

At this level: Expert practitioner. Produces high‑quality, maintainable code and sets the standard for others. Introduces automation, testing, and deployment best practices across the team.

Data Quality & Observability – Monitoring, validation, reliability, incident handling.

At this level: Leads implementation of robust quality frameworks. Moves beyond basic checks to proactive monitoring and anomaly detection. Ensures trust in data outputs.

Technical Leadership – Mentoring, design authority, technical direction.

At this level: Acts as the go‑to technical authority. Guides engineers in design decisions, challenges approaches, and elevates the overall technical capability of the team.

Problem Solving & Decision Making – Breaking down complex technical challenges.

At this level: Resolves ambiguous problems independently. Proposes clear, structured solutions and articulates trade‑offs effectively.

Collaboration & Influence – Working across teams, translating needs into solutions.

At this level: Partners effectively with non‑engineering stakeholders. Ensures solutions meet business needs without compromising technical integrity.

Job Benefits

We offer a fast‑paced, fun, and social working environment where you can truly make an impact. We believe work should enhance and complement your life, which is why we provide a flexible hybrid working model. While there are expectations to attend the London campus periodically, you and your manager will determine the most appropriate balance together, ensuring both your needs and the needs of the business are met. We work hard at UNiDAYS, and we believe in fair compensation for that hard work. That’s why we’re proud to offer all employees full access to our comprehensive benefits package.

Our Perks Include

• 25 days holiday per year increasing with length of service, plus bank holidays
• Competitive salaries
• 4pm finishes every Friday
• Company pension scheme
• Private health insurance (BUPA)
• Dental Insurance (BUPA)
• Income protection policy
• Life assurance policy
• Employee Assistance Program
• Enhanced parental leave pay
• Regular team building activities
• £150 towards your home office set up

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status or disability status.