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- Tasks: Design and develop scalable data pipelines using Python and AWS tools.
- Company: Join FTSE Russell, a leader in financial data solutions.
- Benefits: Competitive salary, flexible work options, and continuous learning opportunities.
- Why this job: Shape the future of data platforms and make an impact in finance.
- Qualifications: 10+ years experience in data engineering and strong Python skills.
- Other info: Collaborative Agile environment with room for growth and innovation.
The predicted salary is between 48000 - 72000 £ per year.
We are building the next-generation data platform at FTSE Russell — and we want you to shape it with us. Your role will involve:
- Designing and developing scalable, testable data pipelines using Python and Apache Spark
- Orchestrating data workflows with AWS tools like Glue, EMR Serverless, Lambda, and S3
- Applying modern software engineering practices: version control, CI/CD, modular design, and automated testing
- Contributing to the development of a lakehouse architecture using Apache Iceberg
- Collaborating with business teams to translate requirements into data-driven solutions
- Building observability into data flows and implementing basic quality checks
- Participating in code reviews, pair programming, and architecture discussions
- Continuously learning about the financial indices domain and sharing insights with the team
WHAT YOU'LL BRING:
- Writes clean, maintainable Python code (ideally with type hints, linters, and tests like pytest)
- Understands data engineering basics: batch processing, schema evolution, and building ETL pipelines
- Has experience with or is eager to learn Apache Spark for large-scale data processing
- Is familiar with the AWS data stack (e.g. S3, Glue, Lambda, EMR)
- Enjoys learning the business context and working closely with stakeholders
- Works well in Agile teams and values collaboration over solo heroics
Nice-to-haves:
- Experience with Apache Iceberg or similar table formats
- Familiarity with CI/CD tools like GitLab CI, Jenkins, or GitHub Actions
- Exposure to data quality frameworks like Great Expectations or Deequ
- Curiosity about financial markets, index data, or investment analytics
AWS Data Engineer in London employer: SoftNice
Contact Detail:
SoftNice Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AWS Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. 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 pipelines, projects, or any relevant work you've done. This is your chance to demonstrate your expertise in Python, Apache Spark, and AWS tools—make it shine!
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your experience with data engineering practices. Practice common interview questions and think about how you can relate your past work to the role at FTSE Russell.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team and shaping the future of data together.
We think you need these skills to ace AWS Data Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your 10+ years of experience and showcases your skills in Python, Apache Spark, and AWS tools. We want to see how your background aligns with the role, so don’t be shy about emphasising relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about building the next-generation data platform with us. Share specific examples of how you've designed scalable data pipelines or collaborated with teams to deliver data-driven solutions.
Show Off Your Technical Skills: When filling out your application, make sure to mention your experience with CI/CD practices, version control, and any familiarity with data quality frameworks. We love seeing candidates who are not just technically proficient but also eager to learn and grow!
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. Plus, we can’t wait to see what you bring to the table!
How to prepare for a job interview at SoftNice
✨Know Your Tech Stack
Make sure you’re well-versed in the AWS tools mentioned in the job description, like Glue, EMR Serverless, and Lambda. Brush up on your Python skills, especially with clean coding practices and testing frameworks like pytest. Being able to discuss your experience or eagerness to learn Apache Spark will definitely impress.
✨Showcase Your Collaboration Skills
Since the role emphasises teamwork, be ready to share examples of how you've collaborated with business teams or participated in Agile environments. Talk about your experiences in code reviews or pair programming, as this shows you value collaboration over working in isolation.
✨Understand the Business Context
Demonstrate your curiosity about the financial indices domain. Research FTSE Russell and be prepared to discuss how data engineering can drive business solutions. Showing that you understand the bigger picture will set you apart from other candidates.
✨Prepare for Technical Questions
Expect technical questions related to data pipelines, ETL processes, and schema evolution. Practice explaining these concepts clearly and concisely. You might also want to prepare for a coding challenge, so brush up on your problem-solving skills and be ready to write some Python code on the spot.
