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For Companies At a Glance
- Tasks: Design and build scalable data pipelines using modern tools like Azure Databricks and Apache Spark.
- Company: Join a dynamic team focused on evolving data platforms in a collaborative environment.
- Benefits: Competitive salary up to £70,000 plus an excellent package and hybrid working options.
- Why this job: Make a meaningful impact in data engineering while working with cutting-edge technologies.
- Qualifications: Experience with Azure Databricks, Apache Spark, SQL, and Python is essential.
- Other info: Fast-paced Agile/DevOps environment with opportunities for professional growth.
The predicted salary is between 42000 - 84000 £ per year.
Job Description
- Permanent
- Basingstoke (Hybrid – x2 PW)
- Up to £70,000 + Excellent Package
OverviewWe're looking for a skilled Data Analytics Engineer to help drive the evolution of our clients data platform. This role is ideal for someone who thrives on building scalable data solutions and is confident working with modern tools such as Azure Databricks, Apache Kafka, and Spark. In this role, you'll play a key part in designing, delivering, and optimising data pipelines and architectures. Your focus will be on enabling robust data ingestion and transformation to support both operational and analytical use cases. If you're passionate about data engineering and want to make a meaningful impact in a collaborative, fast-paced environment, we want to hear from you !!Role and Responsibilities
- Designing and building scalable data pipelines using Apache Spark in Azure Databricks
- Developing real-time and batch data ingestion workflows, ideally using Apache Kafka
- Collaborating with data scientists, analysts, and business stakeholders to build high-quality data products
- Supporting the deployment and productionisation of machine learning pipelines
- Contributing to the ongoing development of a Lakehouse architecture
- Working in an Agile/DevOps environment to continuously improve platform performance and reliability
Essential Skills and ExperienceWe're seeking candidates who bring strong technical skills and a hands-on approach to modern data engineering. You should have:
- Proven experience with Azure Databricks and Apache Spark
- Working knowledge of Apache Kafka and real-time data streaming
- Strong proficiency in SQL and Python
- Familiarity with Azure Data Services and CI/CD pipelines in a DevOps environment
- Solid understanding of data modelling techniques (e.g., Star Schema)
- Excellent problem-solving skills and a high attention to detail
Desirable:
- Azure Data Engineer certification
- Experience working with unstructured data sources (e.g. voice)
- Exposure to Power BI for downstream reporting (desirable, but secondary to platform engineering skills)
- Previous experience in regulated industries
Data Analytics Engineer employer: Intec Select Limited
Contact Detail:
Intec Select Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Analytics Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, especially those using Azure Databricks and Apache Spark. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering questions and scenarios. Practice explaining your thought process when designing data pipelines – it’s all about demonstrating your problem-solving skills!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented Data Analytics Engineers, and applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Data Analytics Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Analytics Engineer role. Highlight your experience with Azure Databricks, Apache Spark, and any relevant projects that showcase your skills in building scalable data solutions.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how your skills align with our needs. Don’t forget to mention your experience with real-time data streaming and collaboration with stakeholders.
Showcase Your Technical Skills: Be specific about your technical skills in your application. Mention your proficiency in SQL and Python, and any experience you have with CI/CD pipelines in a DevOps environment. This will help us see how you can contribute to our team.
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to submit all your documents in one go. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at Intec Select Limited
✨Know Your Tools Inside Out
Make sure you’re well-versed in Azure Databricks, Apache Spark, and Kafka. Brush up on your SQL and Python skills too! Being able to discuss specific projects where you've used these tools will show your hands-on experience.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled complex data challenges in the past. Think about situations where you optimised data pipelines or improved performance. This will demonstrate your analytical thinking and attention to detail.
✨Understand the Business Context
Research the company and its data needs. Be ready to discuss how your role as a Data Analytics Engineer can contribute to their goals. This shows that you’re not just technically skilled but also understand the bigger picture.
✨Be Ready for Collaboration Questions
Since this role involves working with data scientists and business stakeholders, prepare to talk about your experience in collaborative environments. Share examples of how you’ve successfully worked in Agile/DevOps settings to deliver high-quality data products.
