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- Tasks: Lead the design and implementation of scalable data solutions and mentor a talented engineering team.
- Company: Join a dynamic tech company that values innovation and collaboration.
- Benefits: Enjoy flexible working, competitive salary, and comprehensive benefits including health insurance and generous holiday.
- Other info: Be part of a fun, fast-paced environment with excellent career growth opportunities.
- Why this job: Make a real impact in data engineering while shaping the future of our data platform.
- Qualifications: Extensive experience in data engineering, SQL, and cloud platforms like AWS.
The predicted salary is between 70000 - 90000 £ per year.
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
- 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
- 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
- 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
- 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
- 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
- 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.
- Programming & Automation – SQL, dbt, Python, orchestration, CI/CD.
- Data Quality & Observability – Monitoring, validation, reliability, incident handling.
- Technical Leadership – Mentoring, design authority, technical direction.
- Problem Solving & Decision Making – Breaking down complex technical challenges.
- Collaboration & Influence – Working across teams, translating needs into solutions.
Job Benefits
- 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.
Lead Data Engineer employer: UNiDAYS
Contact Detail:
UNiDAYS Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field and let them know you're on the lookout for opportunities. A personal recommendation can go a long way in landing that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those involving scalable data pipelines and cloud platforms. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your SQL and ELT tools knowledge. Practice common data engineering problems and be ready to discuss your past experiences in detail. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Tailor your application to highlight how your experience aligns with our needs, and you'll stand out from the crowd.
We think you need these skills to ace Lead Data Engineer
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience in data engineering, especially with scalable data pipelines and cloud platforms like AWS. We want to see how your skills align with the role of Lead Data Engineer!
Showcase Your Technical Skills: Don’t hold back on showcasing your SQL expertise and familiarity with tools like dbt or Airflow. We’re looking for someone who can demonstrate their technical prowess, so include specific examples of projects where you’ve implemented these skills.
Highlight Leadership Experience: Even though this is a senior individual contributor role, we value technical leadership. Share instances where you've mentored others or led technical discussions, as this will show us your ability to guide and uplift the team.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our company culture!
How to prepare for a job interview at UNiDAYS
✨Know Your Data Inside Out
As a Lead Data Engineer, you need to demonstrate your deep expertise in data engineering. Brush up on your knowledge of SQL, ELT tools like dbt, and cloud platforms such as AWS. Be ready to discuss specific projects where you've designed scalable data pipelines or optimised data models.
✨Showcase Your Technical Leadership
This role requires strong technical leadership, so prepare examples of how you've mentored engineers or led technical discussions. Think about times when you’ve set engineering standards or resolved complex technical challenges, and be ready to share these experiences during the interview.
✨Understand the Business Context
It's crucial to translate business needs into technical solutions. Familiarise yourself with the company's goals and how your role can contribute. Be prepared to discuss how you've partnered with analytics or data science teams to ensure that data solutions are fit for purpose.
✨Prepare for Problem-Solving Questions
Expect to face questions that assess your problem-solving skills. Think of complex technical challenges you've encountered and how you approached them. Be ready to articulate your thought process and the trade-offs you considered in your solutions.