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- Tasks: Design and maintain data pipelines, collaborate on data strategies, and ensure data quality.
- Company: Join a leading organisation in Financial Services, known for innovation and excellence.
- Benefits: Enjoy hybrid work options, competitive salary, and opportunities for professional growth.
- Why this job: Be part of a dynamic team modernising data solutions with real business impact.
- Qualifications: Bachelor’s degree in a related field and experience with Azure, Python, and SQL required.
- Other info: This role offers visibility across the business and involvement in mission-critical projects.
The predicted salary is between 43200 - 72000 £ per year.
Our client, a leading organisation within the Financial Services space, is seeking a talented Data Engineer to join their high-performing team. This is an exciting opportunity for a skilled data professional to play a critical role in modernising legacy platforms, delivering robust data solutions, and contributing to business-wide insight initiatives.
As a Data Engineer, you will be instrumental in implementing new technologies, designing and maintaining scalable ETL processes, and developing and administering cloud-based data platforms. This role requires a proactive individual with a passion for data quality, cloud technologies, and continuous improvement.
Key Responsibilities:- Design, build, and maintain high-performance data pipelines and APIs.
- Collaborate cross-functionally to understand business needs and align data strategies accordingly.
- Assemble and optimise large, complex data sets from diverse sources using SQL, dbt, and Azure technologies.
- Champion data quality and promote a data-as-a-product mindset.
- Define and document technical architecture, including cloud solutions and system diagrams.
- Develop and document test scripts to support robust, reliable deployments.
- Support troubleshooting and continuous improvement in all areas of data management.
- Participate actively in architectural discussions and system evaluations.
- Foster a strong sense of ownership, urgency, and teamwork within the engineering function.
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- Proven experience with Azure infrastructure, including Infrastructure as Code and CI/CD pipeline development (GitHub / Azure DevOps).
- Strong proficiency in Python and SQL, with a solid understanding of data warehousing and ETL best practices.
- Experience working with both structured and unstructured datasets.
- Demonstrated ability to communicate effectively with technical and non-technical stakeholders.
- Strong influencing skills and experience working in collaborative, cross-functional environments.
This is a fantastic opportunity to join a dynamic, innovative organisation working on mission-critical projects with excellent visibility across the business.
Data Engineer (London) employer: Required IT
Contact Detail:
Required IT Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer (London)
✨Tip Number 1
Familiarise yourself with Azure technologies and tools, especially those mentioned in the job description like Azure DevOps and CI/CD pipelines. Having hands-on experience or relevant projects to discuss can really set you apart during interviews.
✨Tip Number 2
Brush up on your SQL and Python skills, as these are crucial for the role. Consider working on personal projects or contributing to open-source projects that involve data engineering to showcase your abilities.
✨Tip Number 3
Prepare to discuss your experience with data quality and how you've implemented best practices in previous roles. Be ready to provide examples of how you've championed data-as-a-product mindsets in your past work.
✨Tip Number 4
Network with professionals in the financial services sector or join relevant online communities. Engaging with others in the field can provide insights into the company culture and may even lead to referrals.
We think you need these skills to ace Data Engineer (London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience and skills that align with the Data Engineer role. Emphasise your proficiency in Azure, Python, SQL, and any experience with ETL processes or cloud technologies.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for data quality and cloud technologies. Mention specific projects where you've implemented data solutions or improved data management processes, demonstrating your proactive approach.
Highlight Relevant Projects: In your application, include examples of past projects that involved designing and maintaining data pipelines or working with complex datasets. This will help illustrate your hands-on experience and technical capabilities.
Showcase Communication Skills: Since the role requires effective communication with both technical and non-technical stakeholders, provide examples in your application where you've successfully collaborated across teams or influenced decisions in a cross-functional environment.
How to prepare for a job interview at Required IT
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Azure, Python, and SQL in detail. Bring examples of past projects where you designed ETL processes or worked with cloud-based data platforms, as this will demonstrate your hands-on expertise.
✨Understand the Business Context
Research the financial services industry and the specific challenges it faces regarding data management. This knowledge will help you align your answers with the company's goals and show that you can contribute to their business-wide insight initiatives.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills and ability to work collaboratively. Think of scenarios where you had to troubleshoot data issues or optimise data pipelines, and be ready to explain your thought process and outcomes.
✨Demonstrate a Passion for Data Quality
Express your commitment to data quality and continuous improvement. Share examples of how you've championed data-as-a-product mindsets in previous roles, as this aligns with the company's focus on robust data solutions.
