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- Tasks: Design and develop high-performance data pipelines and support analytics infrastructure.
- Company: Join Peaple Talent, a dynamic partner in the tech industry, based in Bristol.
- Benefits: Enjoy a full-time role with opportunities for remote work and professional growth.
- Why this job: Be part of an innovative team that values collaboration and continuous improvement.
- Qualifications: Experience with SQL, Python, and data pipeline architecture is essential.
- Other info: Ideal for mid-senior level candidates looking to make an impact in technology.
The predicted salary is between 60000 - 84000 £ per year.
🧱 Databricks Data Engineer | London | £50,000 – £70,000 🧱 Peaple Talent have partnered with a specialist data consultancy delivering services across data engineering, data strategy, data migration, BI
Data Engineer employer: Peaple Talent
Contact Detail:
Peaple Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as SQL, Python, and PySpark. Being able to discuss your hands-on experience with these tools during interviews will demonstrate your capability and readiness for the role.
✨Tip Number 2
Engage with the Agile community by participating in relevant forums or local meetups. This not only helps you stay updated on best practices but also allows you to network with professionals who might provide insights or referrals for the Data Engineer position.
✨Tip Number 3
Showcase your collaborative skills by discussing past projects where you worked closely with multidisciplinary teams. Highlighting your ability to gather requirements and tailor solutions will resonate well with the hiring team.
✨Tip Number 4
Stay current with emerging technologies related to data engineering. Consider building a small project or prototype using cloud-native services like AWS or Azure to demonstrate your initiative and practical knowledge during interviews.
We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience and skills that align with the Data Engineer role. Focus on your hands-on expertise with SQL, Python, and PySpark, as well as any experience with Agile frameworks and data pipeline architecture.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the position and the company. Mention specific projects or experiences that demonstrate your ability to design and maintain data pipelines, and how you can contribute to their analytics infrastructure.
Showcase Technical Skills: Clearly list your technical skills in your application. Include your proficiency with tools like Azure DevOps, Apache AirFlow, and cloud services such as AWS. Providing examples of how you've used these tools in past roles can strengthen your application.
Highlight Collaboration Experience: Since the role involves working closely with multidisciplinary teams, emphasise any previous collaborative projects. Discuss how you’ve partnered with other engineers or teams to deliver impactful data solutions, showcasing your teamwork and communication skills.
How to prepare for a job interview at Peaple Talent
✨Showcase Your Technical Skills
Be prepared to discuss your hands-on experience with SQL, Python, and PySpark. Bring examples of data pipelines you've built or optimised, and be ready to explain the challenges you faced and how you overcame them.
✨Understand Agile Methodologies
Since the role involves participating in Agile team practices, brush up on Agile principles and be ready to discuss your experience with tools like Azure DevOps. Highlight any specific contributions you've made in Agile environments.
✨Demonstrate Problem-Solving Abilities
Expect to face questions about diagnosing and resolving data challenges. Prepare to share specific instances where you improved system performance or data integrity, showcasing your analytical skills.
✨Communicate Complex Concepts Simply
You'll need to translate complex engineering concepts for non-technical audiences. Practice explaining your past projects in a way that anyone can understand, focusing on the impact of your work rather than just the technical details.
