At a Glance
- Tasks: Build and optimise data pipelines using cutting-edge tools like Databricks and Microsoft Fabric.
- Company: Join Leonard Curtis, a leading professional services provider with a focus on innovation.
- Benefits: Enjoy 25 days holiday, birthday leave, and community support days.
- Other info: Dynamic environment with diverse career pathways and strong learning opportunities.
- Why this job: Make an impact by delivering clean, production-ready data for enterprise analytics.
- Qualifications: Proficiency in PySpark, SQL, and experience with data pipeline orchestration.
The predicted salary is between 60750 - 74250 £ per year.
We're currently seeking a Data Engineer to build, maintain, and optimise robust data pipelines.
This role develops efficient ETL/ELT workflows using both Microsoft Fabric tools and the Databricks Lakehouse Platform to deliver clean, production-ready data for enterprise analytics.
- Role Requirements
- Strong proficiency in Py Spark, Databricks SQL, T-SQL, and handling Delta Lake formats.
- Hands-on experience building production pipelines inside Databricks workspaces and Fabric capacities.
- Practical knowledge of structural data formats including Parquet, JSON, and CSV.
- Experience with orchestration tools, scheduling dependencies, and error handling in a cloud environment.
- Familiarity with unit testing data pipelines and implementing automated data quality checks.
- Role Responsibilities
- Construct scalable data ingestion pipelines using Fabric Data Factory, Dataflow Gen2, and Databricks Workflows.
- Implement Delta Live Tables (DLT) to automate batch and real-time streaming data processing.
- Build and maintain the Medallion Architecture silver & gold layer across shared storage.
- Write optimized transformations in Fabric Notebooks and Databricks clusters using Py Spark and SQL.
- Configure data access, masking, and governance policies within Databricks Unity Catalog.
- Optimise Lakehouse table performance using Z-Order indexing, liquid clustering, and file compaction techniques.
- Deploy data pipelines using CI/CD frameworks, Git integration, and Databricks Asset Bundles (DABs).
- Prepare finalised gold layer datasets to support real-time Power BI Direct Lake reporting model
- Salary aligned with your skills and expertise
- 25 days holiday allowance plus statutory public holidays and the option to purchase more
- Birthday leave
- 2 giving back days per year. We encourage our team to support the wider community by providing paid leave to work with local charities or good causes
- A hard working, fun and professional working environment
- Enhanced family friendly policies, including enhanced Maternity pay
Leonard Curtis is a market leading professional services provider operating across the UK and offshore.
Since our formation we’ve supported business owners and advisors by listening and offering practical solutions and tailored advice.
Our expert team of specialists deliver positive strategic advice across restructuring and insolvency, funding, legal, business services and M&A advisory across 30 offices.
A career with Leonard Curtis will open the door to varied career pathways.
We have built an environment that empowers you to express yourself to have confidence in who you are and what you’re capable of and develop the career you want.
Learning and Development
Leonard Curtis has a wealth of resources available to help you develop your career from the moment you join.
Activities range from on-line learning modules to external training and qualifications.
Diversity and Inclusion
Diversity is a core business imperative of the Group.
We are an equal opportunities employer which promotes inclusiveness and always employ the best professional for the job.
Having a diverse workforce allows the Group to draw upon a range of different ideas and experiences which supports our business’s growth and creates an environment where everyone has an equal opportunity for success.
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Data Engineer in Whitefield employer: Leonard Curtis
Leonard Curtis is an excellent employer, offering a dynamic and collaborative work environment in Sheffield that fosters professional growth and development. With a focus on building strong relationships within the professional services sector, employees benefit from clear career progression opportunities and the chance to engage with senior decision-makers in a supportive atmosphere. The company values its team members, providing them with the resources and encouragement needed to thrive in their roles.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer in Whitefield
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Leonard Curtis!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Engineer at Leonard Curtis.
✨Leverage Professional Networks
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 Leonard Curtis.
✨Apply Directly through Our Website
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We think you need these skills to ace Data Engineer in Whitefield
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 Leonard Curtis, 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 Leonard Curtis. 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 Leonard Curtis
✨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 Leonard Curtis!
✨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.
