At a Glance
- Tasks: Design and maintain scalable data pipelines using Azure technologies.
- Company: Join Paul Smith, a leading independent design company known for creativity.
- Benefits: Enjoy a clothing allowance, health insurance, and 25 days annual leave.
- Other info: Inclusive culture that values diverse backgrounds and offers excellent career growth.
- Why this job: Make an impact in a creative environment while working with cutting-edge data technologies.
- Qualifications: Degree in Data Engineering or relevant experience; strong SQL and Azure skills required.
The predicted salary is between 50000 - 65000 £ per year.
Paul Smith is one of Britain’s leading independent design companies. We champion positivity, curiosity and creativity. These qualities underpin every Paul Smith design, whether it’s a shirt, a shop or a special collaboration. Reaffirming the values that Paul set down in 1970, ‘classic with a twist’ remains the guiding principle of the company.
The Role
- Design, build, and maintain scalable ETL/ELT pipelines across Azure Data Factory, Synapse, Dataflows Gen2, and Microsoft Fabric.
- Ingest data from diverse sources (REST APIs, SFTP, on‑prem systems, SQL Server, cloud applications, flat files, JSON, CSV, Parquet).
- Implement medallion/lakehouse architecture (Bronze, Silver, Gold) using Delta Lake, Fabric Lakehouse, or Databricks.
- Develop and optimise SQL, Python, and PySpark‑based transformations for efficient data processing.
- Support the build and operation of Azure cloud data services including Azure Data Lake Storage (ADLS Gen2), Azure Synapse Analytics, Azure Functions, Logic Apps, Key Vault, and DevOps CI/CD pipelines.
- Contribute to scalable, secure, and cost‑optimised platform design in collaboration with BI and IT.
- Apply strong data management practices including data validation, DQ monitoring, metadata completeness, and schema enforcement.
- Use tools such as Microsoft Purview, Fabric monitoring, or custom frameworks to maintain governance, lineage, and compliance.
- Ensure data reliability, resilience, and integrity across all datasets and pipelines.
- Build and maintain curated, well‑modelled datasets for analytics and Power BI semantic models.
- Collaborate with analysts to define KPIs, business rules, and data structures that support reporting consistency.
- Provide technical guidance on Power BI data modelling, DAX optimisation, and dataset performance.
- Partner with business stakeholders to understand data needs and translate them into scalable technical solutions.
- Work cross‑functionally with teams across Digital, Retail, eCommerce, Merchandising, Marketing, and Finance.
- Contribute to documentation, knowledge sharing, and mentoring of junior team members when required.
Your Skills & Experience
- Degree in Data Engineering, Computer Science, Analytics, or relevant discipline (or equivalent hands‑on experience).
- Industry certifications beneficial.
- Strong SQL experience (T‑SQL, stored procedures, performance tuning).
- Experience building ETL/ELT pipelines in Azure Data Factory, Synapse, or Fabric Dataflows.
- Hands‑on experience with Microsoft Fabric (Lakehouse, Data Engineering, Data Pipelines).
- Strong understanding of Azure data services: ADLS, Synapse, Functions, Key Vault.
- Proficiency in Python and/or PySpark for data engineering tasks.
- Experience modelling data structures for reporting and analytics (Kimball, Star Schema, SCD1/2).
- Experience working with Power BI datasets and data modelling.
- Experience with Databricks, Delta Lake and medallion architecture is desirable.
- Familiarity with CI/CD pipelines using Azure DevOps or GitHub.
- Knowledge of data governance frameworks and tools such as Purview.
- Exposure to AI‑driven data engineering patterns (embeddings, LLM‑based retrieval).
- Understanding of cost optimisation and cloud resource management.
What’s In It For You
- A generous seasonal clothing allowance.
- A discretionary discount on Paul Smith products.
- Private health insurance with Vitality.
- Wellbeing support through the Retail trust for you and your family.
- An annual leave entitlement of 25 days, plus Bank Holidays.
- Cycle to work and travel loan schemes.
- SmartTech.
- A day for change.
Find out more about our company culture and the full list of benefits we can offer you. At Paul Smith, we celebrate and encourage applications from all walks of life into our growing workforce. We are committed to empowering people from any background, belief system, or ethnicity, offering an inclusive environment where talent is recognised and valued.
Data Engineer in Nottingham employer: Paul Smith
At Paul Smith, we pride ourselves on being an exceptional employer that fosters a culture of positivity, curiosity, and creativity. Our commitment to employee wellbeing is reflected in our generous benefits package, including a seasonal clothing allowance, private health insurance, and a supportive environment for personal growth and development. Located in the heart of Britain’s fashion scene, we offer unique opportunities to work collaboratively across diverse teams, ensuring that every voice is heard and valued.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer in Nottingham
✨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 Paul Smith!
✨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 Paul Smith.
✨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 Paul Smith.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer at Paul Smith, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Engineer in Nottingham
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 Paul Smith, 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 Paul Smith. 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 Paul Smith
✨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 Paul Smith!
✨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.
