At a Glance
- Tasks: Design and develop scalable data pipelines and models for AI initiatives.
- Company: Join a growing AI & Data team in a hybrid work environment.
- Benefits: Competitive salary, benefits, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on best practices and innovation.
- Why this job: Make an impact by building modern data platforms with cutting-edge technologies.
- Qualifications: Strong experience with Databricks, Python, SQL, and data warehousing.
The predicted salary is between 70000 - 80000 £ per year.
Senior Data Engineer
Location
Hybrid (2 Days office)
Type
Permanent
Salary
About the Role
We're looking for a Senior Data Engineer to join a growing AI & Data team, building modern data platforms using Databricks and Microsoft Fabric.
You'll design and develop scalable data pipelines, data models and lakehouse solutions that support reporting, analytics and AI initiatives.
Key Responsibilities
- Lead data pipeline architecture development/orchestration/maintenance.
- Develop robust data models and ETL/ELT processes.
- Optimise data performance and troubleshoot pipeline issues.
- Build and maintain data warehouse/lakehouse solutions.
- Collaborate with cross-functional teams and document technical solutions.
- Support best practices around data quality, governance and CI/CD.
- Required Skills
- Strong experience with Databricks.
- Excellent Python and SQL skills.
- Good knowledge of Spark, Py Spark, Delta Lake, Delta Live Tables, Unity Catalog and Medallion Architecture.
- Experience with Azure Dev Ops, Git and CI/CD.
- Strong understanding of data warehousing, star schema, fact & dimension modelling, SCD and CDC.
- Experience building data platforms for BI, reporting and analytics.
- Exposure to AI development tools such as Cursor is desirable.
- Nice to Have
- Azure certifications (AZ-900, DP-900, DP-203, DP-500).
- Databricks Lakehouse Data Engineer certification.
- MSc in Data Science or a related field.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer
✨Get Involved in Data Science Meetups
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✨Show Off Your Projects
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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 Careerwise.
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We think you need these skills to ace Senior 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 Careerwise, 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 Careerwise. 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 Careerwise
✨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 Careerwise!
✨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.
