At a Glance
- Tasks: Lead analytics engineering, transforming court data into high-quality products for analysis and decision-making.
- Company: Join HMCTS, a forward-thinking organisation committed to justice and innovation.
- Benefits: Enjoy 25 days annual leave, a competitive pension scheme, and flexible working options.
- Other info: Embrace a culture of diversity and continuous learning in a supportive environment.
- Why this job: Make a real impact in the justice system while leading a dynamic team of experts.
- Qualifications: Proven experience in analytics engineering and strong skills in Azure, Python, and SQL.
The predicted salary is between 55000 - 65000 € per year.
The Head of Analytics Engineering will lead the analytics‑engineering function within HMCTS, transforming court and tribunal data into trusted, high‑quality data products. The role delivers technical and delivery leadership for analytics‑focused data engineering, ensuring analytical datasets are well‑modelled, quality assured, reusable, and clearly documented.
Responsibilities
- Lead and develop the Analytics Engineering function, setting a clear direction for analytics‑focused data engineering across HMCTS.
- Own the delivery of analytical data products used by analysts, modellers, machine learning engineers, management information teams and approved researchers.
- Lead innovation in analytics engineering, ensuring the team explores and adopts new tools and capabilities, including AI and emerging platform features, to improve quality, efficiency and reuse.
- Lead the development and adoption of shared analytics engineering standards, building collective ownership and consistency across the team.
- Act as the senior technical leader and head of profession for analytics engineering in HMCTS.
- Lead the adoption and embedding of agile delivery methodologies within the analytics engineering team, shaping ways of working that support iterative delivery, clear prioritisation and continuous user feedback.
- Define, implement and enforce the use of data contracts to clearly specify the structure, quality, timeliness and ownership expectations for data used for analytical data products, working with Digital and Technology Services and data producers.
- Act as the primary interface for machine learning and data science, ensuring that analytics engineering provides stable, well‑governed and model‑ready datasets to support safe model deployment and monitoring.
- Provide consistent leadership to your area of the business and define and implement objectives, aligned with HMCTS and Civil Service priorities.
- Identify trends, remove performance barriers and make strategic people decisions.
- Promote an inclusive culture that supports diversity and creates a safe working environment.
Qualifications (Essential)
- Significant experience in analytics engineering or analytics‑focused data engineering, delivering data products for analysis, modelling and management information.
- Strong expertise in designing analytical data models, use of dimensional modelling techniques and structuring data to support reuse, performance reporting and advanced analytics.
- Proven experience setting, maintaining and assuring technical standards across analytics engineering teams, rather than relying on individual delivery.
- Strong practical expertise in the Microsoft Azure data and analytics technology stack, experience using Python for data transformation and analytical workflows and strong SQL skills for querying, transforming and validating large analytical datasets.
- Experience using GitHub (or equivalent) for version control, peer review and collaborative development within analytics or data engineering teams.
- Strong understanding of data quality, testing, validation and controlled release practices.
- Experience leading and developing multidisciplinary technical teams, including capability development and performance management.
- Ability to explain complex data and technical concepts clearly to non‑technical audiences.
- Experience working in complex organisational environments with multiple stakeholders and competing priorities.
- Strategic leadership and people management skills to coach and empower managers to resolve complex people issues and build high‑performing teams.
Desirable Skills
- Knowledge of the justice system and the work of HMCTS.
- Experience delivering data products using agile delivery methodologies, including working in multidisciplinary teams, managing backlogs and iterating based on user needs.
- Experience designing or working with data contracts, or similar mechanisms to manage expectations between data producers and consumers.
- Experience working with large and complex operational or administrative datasets.
- Familiarity with data governance, master data and reference data concepts.
Behaviours
- Changing & Improving
- Leadership
- Working Together
- Delivering at Pace
Working Pattern & Travel
Minimum working hours are 30 hours over 4 days a week. Occasionally travel may be required to HMCTS sites to support project delivery.
Benefits
- Annual leave: 25 days on appointment, increasing to 30 days after five years’ service.
- Highly competitive contributory pension scheme.
- Training and development opportunities.
- Employee support networks and inclusive workplace culture.
- Flexible working policy and family‑friendly benefits.
EEO Statement
The Ministry of Justice is an inclusive employer committed to diversity and equal opportunities. All staff are entitled to equal treatment and reasonable adjustments will be made during the recruitment process where needed.
Head of Analytics Engineering in London employer: Ministry of Justice
HMCTS is an exceptional employer, offering a dynamic work environment where innovation in analytics engineering thrives. With a strong commitment to employee growth, competitive benefits including generous annual leave and a contributory pension scheme, and a culture that prioritises inclusivity and flexibility, HMCTS provides a meaningful opportunity for professionals to lead impactful data initiatives within the justice system.
StudySmarter Expert Advice🤫
We think this is how you could land Head of Analytics Engineering in London
✨Tip Number 1
Network like a pro! Reach out to people in the analytics engineering field, especially those at HMCTS. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio or case studies showcasing your previous work in analytics engineering. This is your chance to demonstrate how you've transformed data into high-quality products.
✨Tip Number 3
Get ready for the interview! Research common questions for leadership roles in analytics engineering and practice your responses. Be prepared to discuss how you’ve led teams and driven innovation.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take the initiative to engage directly with us.
We think you need these skills to ace Head of Analytics Engineering in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience in analytics engineering. Use keywords from the job description to show that you understand what we're looking for.
Showcase Your Technical Skills:Don’t hold back on detailing your expertise with the Microsoft Azure stack, Python, and SQL. We want to see how you've used these tools to deliver high-quality data products in your previous roles.
Demonstrate Leadership Experience:Since this role is all about leading a team, share examples of how you've developed multidisciplinary teams and managed performance. We love to see how you've empowered others to succeed!
Apply Through Our Website:We encourage you to submit your application through our website. It’s the best way to ensure your application gets the attention it deserves, and we can’t wait to hear from you!
How to prepare for a job interview at Ministry of Justice
✨Know Your Data Inside Out
Make sure you’re well-versed in analytics engineering concepts, especially around data modelling and quality assurance. Brush up on your experience with the Microsoft Azure stack, Python, and SQL, as these will likely come up during the interview.
✨Showcase Your Leadership Skills
Prepare examples that highlight your experience in leading multidisciplinary teams and driving innovation in analytics engineering. Be ready to discuss how you've set technical standards and fostered an inclusive culture in previous roles.
✨Understand Agile Methodologies
Since the role involves adopting agile delivery methodologies, be prepared to discuss your experience with agile practices. Share specific instances where you’ve managed backlogs or iterated based on user feedback to improve data products.
✨Communicate Complex Ideas Simply
Practice explaining complex data concepts in a way that non-technical stakeholders can understand. This skill is crucial for the role, so think of examples where you successfully communicated technical information to diverse audiences.
