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- Tasks: Build and manage scalable data pipelines to drive business insights.
- Company: Leading digital optimisation company with a focus on innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Join a team that shapes the future of data-driven decision making.
- Qualifications: 5+ years in data engineering, strong SQL and AWS skills required.
- Other info: Exciting projects that enhance operational efficiency and career advancement.
The predicted salary is between 36000 - 60000 £ per year.
A leading digital optimization company is seeking a Senior Data Engineer to develop and manage data pipelines. Candidates should have over 5 years of experience in data engineering, expertise in ETL processes, and strong skills in SQL and AWS technologies. This position offers opportunities to work on innovative data projects that will drive business insights and operational efficiency. A Bachelor's or Master's degree in Computer Science is required.
Senior Data Engineer — Build Scalable Data Pipelines employer: Optimizely
Contact Detail:
Optimizely Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer — Build Scalable Data Pipelines
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field and let them know you're on the lookout for opportunities. You never know who might have a lead or can refer you directly to hiring managers.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best data pipelines and projects. This is your chance to demonstrate your expertise in ETL processes, SQL, and AWS technologies, making you stand out from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering questions and scenarios. Practice explaining your thought process when building scalable data pipelines, as this will show your problem-solving skills and technical knowledge.
✨Tip Number 4
Don't forget to apply through our website! We make it easy for you to find and apply for the Senior Data Engineer role. Plus, it shows you're genuinely interested in joining our team and working on innovative data projects.
We think you need these skills to ace Senior Data Engineer — Build Scalable Data Pipelines
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data engineering, especially with ETL processes and SQL. We want to see how your skills align with the role, 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 passionate about data engineering and how your background makes you a perfect fit for our team. We love hearing your story!
Showcase Your Technical Skills: Since we’re looking for someone with strong AWS skills, make sure to mention any relevant certifications or projects. We want to know how you’ve used these technologies to solve real-world problems.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
How to prepare for a job interview at Optimizely
✨Know Your Data Engineering Fundamentals
Make sure you brush up on your data engineering basics, especially ETL processes. Be ready to discuss how you've implemented these in past projects, as well as any challenges you've faced and how you overcame them.
✨Showcase Your SQL Skills
Prepare to demonstrate your SQL expertise during the interview. You might be asked to solve a problem or optimise a query on the spot, so practice common SQL scenarios and be ready to explain your thought process.
✨Familiarise Yourself with AWS Technologies
Since this role requires strong skills in AWS, make sure you know the key services relevant to data engineering, like S3, Redshift, and Glue. Be prepared to discuss how you've used these tools in your previous roles.
✨Highlight Your Problem-Solving Skills
Data engineering often involves troubleshooting and optimising pipelines. Think of specific examples where you've improved efficiency or solved complex data issues, and be ready to share these stories during your interview.
