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- Tasks: Enhance and scale data foundations using SQL and Python for effective pipeline management.
- Company: Leading data management company based in Greater London with a vibrant team.
- Benefits: Flexible working conditions and ample career development opportunities.
- Other info: Remote work available in a supportive and innovative environment.
- Why this job: Join a dynamic team and support analytics and AI initiatives while ensuring data security.
- Qualifications: Proficiency in SQL and Python, with collaboration skills for Engineering and DevOps teams.
The predicted salary is between 36000 - 60000 £ per year.
A leading data management company located in Greater London is seeking a skilled Data Engineer to enhance and scale its data foundations. The role demands proficiency in SQL and Python for effective data pipeline management and collaboration with Engineering and DevOps teams. You’ll work on improving access to platform data and support analytics and AI initiatives while ensuring compliance with security standards. Join a vibrant team with flexible working conditions and ample career development opportunities.
Data Engineer — Pipelines, AWS & dbt (Remote) employer: Convertr
Contact Detail:
Convertr Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer — Pipelines, AWS & dbt (Remote)
✨Tip Number 1
Network like a pro! Reach out to current or former employees in the data engineering field. They can give you insider info about the company culture and maybe even refer you for the role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your SQL and Python projects. This will not only demonstrate your technical abilities but also your passion for data engineering.
✨Tip Number 3
Prepare for the interview by brushing up on your knowledge of AWS and dbt. Be ready to discuss how you've used these tools in past projects, as this will show you're the right fit for the team.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take that extra step to connect with us directly.
We think you need these skills to ace Data Engineer — Pipelines, AWS & dbt (Remote)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with SQL and Python, as these are key skills for the Data Engineer role. We want to see how your background aligns with our needs, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the role and how your skills can help us enhance our data foundations. Keep it concise but impactful!
Showcase Your Collaboration Skills: Since you'll be working closely with Engineering and DevOps teams, highlight any past experiences where you successfully collaborated on projects. We love team players who can communicate effectively!
Apply Through Our Website: We encourage you to apply directly through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates!
How to prepare for a job interview at Convertr
✨Know Your Tech Stack
Make sure you’re well-versed in SQL and Python, as these are crucial for the role. Brush up on your knowledge of data pipelines and be ready to discuss how you've used these technologies in past projects.
✨Understand the Company’s Data Needs
Research the company’s data management practices and their current challenges. This will help you tailor your answers to show how you can enhance and scale their data foundations effectively.
✨Collaboration is Key
Since you'll be working with Engineering and DevOps teams, prepare examples of how you've successfully collaborated in the past. Highlight your communication skills and ability to work in a team environment.
✨Security Standards Matter
Familiarise yourself with data security standards relevant to the role. Be prepared to discuss how you ensure compliance in your work, as this is essential for supporting analytics and AI initiatives.