At a Glance
- Tasks: Design and optimise data solutions using Databricks and Azure for impactful projects.
- Company: Join a global software company leading in AI and Data innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on collaboration and continuous learning.
- Why this job: Be at the forefront of data engineering and shape the future of data solutions.
- Qualifications: Experience in data engineering, Databricks, SQL, and Python required.
The predicted salary is between 60000 - 80000 £ per year.
Data Engineer (Databricks) is required by a global software company to join its AI and Data team and play a key role in designing, developing, and maintaining data solutions.
Responsibilities
- Designing, developing, orchestrating, maintaining, and optimizing robust data pipelines and data solutions using Databricks and the Azure ecosystem.
- Building and enhancing structured data models that transform raw data into reliable, business-ready information.
- Implementing data loading, transformation, exploration, and processing solutions.
- Troubleshooting, analysing, and optimizing ETL processes while performing root cause analysis to improve reliability and performance.
- Supporting CI/CD processes and Agile delivery practices using Azure Dev Ops, Repos, and pipeline automation.
- Creating and maintaining technical documentation covering workflows, data models, pipelines, and processes.
Required experience and skills
- Extensive experience in a Data Engineering role delivering data pipelines, data warehouses, data lakes, and lakehouse solutions for BI, reporting, and analytics.
Star Schema, fact and dimension modelling, SCD, and CDC.
- Strong hands-on experience Databricks including Workflows, Delta Live Tables, Delta Sharing, and Unity Catalog.
- Medallion Architecture.
- Apache Spark with Data Frames.
- Experience integrating Databricks with Azure data services, including Azure Data Factory (ADF) and Azure Data Lake Storage Gen2 (ADLS Gen2).
- Advanced SQL and Python.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer/Databricks
✨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 Careerwise!
✨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/Databricks at Careerwise.
✨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 Careerwise.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer/Databricks at Careerwise, 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/Databricks
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.
