At a Glance
- Tasks: Lead a team of Data Engineers to design and deliver innovative data solutions.
- Company: Join Acuity Analytics, a global leader in data transformation.
- Benefits: Competitive salary, career growth, and the chance to work with cutting-edge technologies.
- Other info: Collaborate on high-impact projects and thrive in a people-focused environment.
- Why this job: Shape the future of data engineering while mentoring a talented team.
- Qualifications: 7+ years in data engineering with strong leadership and technical skills.
The predicted salary is between 80000 - 100000 £ per year.
About Us
Ascent has recently been acquired by Acuity Analytics. This is both a significant milestone for us and a tremendous opportunity for you. Acuity Analytics is a business with a strong global reputation, an impressive client base and ambitious growth plans. We deliver deep insights and domain-led digital transformation to high-growth and heavily regulated organisations. To our customers, we bring a partnership that provides the talent, technology and capability to enhance performance and operational efficiency.
About the role
We are looking for an experienced Lead Data Engineer with 7+ years' commercial experience to lead the design, delivery, and continuous evolution of our modern data platform built on Microsoft technologies.
This is a hands-on leadership role, combining technical expertise with people management, where you'll lead a team of Data Engineers while delivering scalable, secure, and high-performing data solutions across Databricks, Microsoft Fabric, Azure Synapse, and Azure.
You'll work closely with architects, stakeholders, and delivery teams to define technical direction, establish engineering best practices, and ensure successful project delivery. This is an opportunity to play a key role in shaping our Data Engineering capability while mentoring and developing a high-performing engineering team.
Key Responsibilities
- Lead and mentor a team of Data Engineers, supporting their technical development and delivery.
- Act as the technical SME for Data Engineering, providing guidance and support to other engineers.
- Lead technical delivery across projects, defining solutions and implementation approaches.
- Design, build, and optimise scalable data platforms using Microsoft Fabric, Databricks, Azure Synapse, and Azure technologies.
- Develop and maintain robust data pipelines using PySpark, Spark SQL, and Azure Data Factory, Synapse, or Fabric Pipelines.
- Scope technical tasks and provide accurate estimates for delivery.
- Review code and establish engineering standards and best practices across the team.
- Build and optimise SQL solutions, including complex queries, stored procedures, and database security.
- Manage Azure DevOps Boards, Repositories, and CI/CD Pipelines throughout the development lifecycle.
- Collaborate with Solution Architects, Product Owners, and stakeholders to deliver high-quality data solutions.
- Drive Agile delivery, sprint planning, technical reviews, and continuous improvement initiatives.
Skills Required
Leadership & Delivery
- Experience leading and mentoring Data Engineering teams.
- Strong stakeholder management and communication skills.
- Experience leading technical projects and Agile delivery teams.
- Ability to scope technical work and define implementation approaches.
- Ownership of code quality, code reviews, and engineering best practices.
Data Engineering
- Strong commercial experience with Microsoft Fabric, Databricks, or Azure Synapse.
- Expert knowledge of PySpark and Spark SQL, with the ability to act as a technical SME for the wider engineering team.
- Strong SQL skills, including query optimisation, stored procedures, and database security.
- Experience building and managing ETL/ELT pipelines.
- Pipeline orchestration using Azure Data Factory, Synapse Pipelines, or Microsoft Fabric.
- Experience developing scalable cloud-based data platforms.
Cloud & DevOps
- Azure DevOps (Boards, Repositories, and CI/CD Pipelines).
- Feature branching, deployment, and release management.
- CI/CD best practices for modern data platforms.
Nice to Have
Emerging Technologies
- Experience with Graph Databases (Neo4j).
- Experience with Snowflake.
- Experience using AI-assisted development tools such as Claude and GitHub Copilot.
- Experience with Azure DevOps Pipelines.
- Experience working across Microsoft data platforms including Fabric, Databricks, and Synapse.
Certifications (Preferred)
- Microsoft Certified: Fabric Analytics Engineer Associate.
- Microsoft Certified: Fabric Data Engineer Associate.
- Databricks Professional Certification.
- Snowflake Certification.
Why Join Us?
- Lead and grow a talented team of Data Engineers.
- Play a key role in shaping our Nearshore Data Engineering capability.
- Work across modern Microsoft technologies including Fabric, Databricks, Synapse, and Azure.
- Blend hands-on technical leadership with people management.
- Collaborate on high-impact enterprise data transformation projects with leading clients.
If you have any questions contact our Talent Acquisition team on ta.admin@acuityanalytics.
For more details about Acuity Analytics please see here: Read here.
Lead Data Engineer employer: Acuity Analytics
At Acuity Analytics, we pride ourselves on being an exceptional employer that values innovation, collaboration, and personal growth. As a Principal Cloud & Platform Engineer, you will be part of a dynamic team that not only tackles complex challenges but also fosters a supportive work culture where your contributions are recognised and your professional development is prioritised. With opportunities to mentor future leaders and influence engineering strategy, you'll find a rewarding environment that encourages continuous improvement and embraces cutting-edge technologies.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Engineer
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Acuity Analytics.
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We think you need these skills to ace Lead Data Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Acuity Analytics, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Acuity Analytics. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Acuity Analytics
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Acuity Analytics!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
