At a Glance
- Tasks: Design and build scalable data pipelines using Databricks, Python, and SQL.
- Company: Join STARK UK, a leader in data transformation and analytics.
- Benefits: Full-time role with competitive salary and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and career development.
- Why this job: Be part of a modernisation journey and make an impact on data-driven decision making.
- Qualifications: Experience in data engineering, Python, and Azure technologies is essential.
The predicted salary is between 63000 - 77000 £ per year.
STARK UK is transforming its data platform and engineering capability to support the next generation of analytics, self-service reporting, AI, automation and data-driven decision making. We are seeking a hands-on Data Engineer to join our Data Engineering team and help build, improve and support the trusted data products that underpin decision making across STARK UK. Reporting to the Lead Data Engineer, you will work within a team moving from Informatica and Azure Synapse-based solutions towards a modern Databricks-centric platform. You will design and build scalable data pipelines, transformations and business-ready datasets, while helping migrate existing solutions and improve engineering standards. The role covers the full data product lifecycle, from source ingestion through Bronze, Silver, Gold and Platinum data assets to curated datasets that support reporting, self-service analytics and future AI initiatives. You will work closely with other Data Engineers, Data Analysts, business stakeholders and delivery partners to turn business requirements into reliable, reusable data products.
This is an opportunity to develop deep Databricks capability while contributing practical experience of Azure Synapse and Python during a significant period of platform modernisation.
Key Responsibilities
- Data Pipeline and Data Product Engineering
- Design, build, test and maintain scalable data pipelines and data products using Databricks, Python, SQL and Azure data technologies.
- Ingest, transform and curate data from operational systems, files, APIs and other internal or external sources.
- Build and maintain data assets across Bronze, Silver, Gold and Platinum layers, following agreed architecture and engineering standards.
- Develop reusable, business-ready datasets that support analytics, reporting, semantic models and self-service use cases.
- Apply appropriate data modelling and transformation techniques to produce clear, performant and maintainable data structures.
- Ensure solutions are documented, version-controlled, testable and supportable.
- Platform Modernisation
- Contribute to the transition from Informatica and Azure Synapse-based solutions to Databricks, including analysis, migration, validation and decommissioning activity.
- Use existing Synapse knowledge to understand current pipelines and architecture, identify dependencies and support safe migration to the target platform.
- Refactor or rebuild legacy data processes where appropriate, improving scalability, maintainability and operational resilience.
- Work with the Lead Data Engineer, Architecture and delivery partners to adopt agreed Databricks patterns and standards.
- Support effective knowledge transfer from System Integrators and other third parties, building internal team capability and reducing long-term dependency.
- Engineering Quality and Operational Excellence
- Build data quality controls, automated tests, monitoring and alerting into engineering solutions.
- Follow DataOps, CI/CD, source-control and deployment practices, contributing to their continued improvement.
- Investigate and resolve data pipeline failures, quality issues and performance problems, including appropriate operational support for production data services.
- Identify technical debt and manual processes and contribute practical improvements that reduce risk and support effort.
- Maintain clear technical documentation, lineage and operational knowledge so that solutions can be understood and supported across the team.
- Ensure engineering work complies with security, governance and data management standards.
- Collaboration and Business Delivery
- Work with Data Analysts and business stakeholders to understand data requirements and translate them into well-designed engineering solutions.
- Partner with other Data Engineers through code reviews, problem solving and knowledge sharing.
- Collaborate with Architecture, Insight.
Data Engineer employer: Raab-Karcher Baustoffhandel
Jewson, part of Stark UK, is an excellent employer that prioritises employee growth and development in a supportive work environment. Located in Redruth, we offer a competitive salary alongside 34 days of holiday, pension, and life assurance, ensuring our team members enjoy a healthy work-life balance while building meaningful relationships with customers.
Contact Details:
Raab-Karcher Baustoffhandel Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
✨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 Raab-Karcher Baustoffhandel!
✨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 Raab-Karcher Baustoffhandel.
✨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 Raab-Karcher Baustoffhandel.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer at Raab-Karcher Baustoffhandel, 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
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 Raab-Karcher Baustoffhandel, 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 Raab-Karcher Baustoffhandel. 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 Raab-Karcher Baustoffhandel
✨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 Raab-Karcher Baustoffhandel!
✨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.
