At a Glance
- Tasks: Design and maintain data pipelines for Treasury applications using large-scale datasets.
- Company: Join a leading financial institution focused on innovation and data-driven solutions.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on data quality and compliance.
- Why this job: Make an impact in finance by optimising data processes and supporting analytics.
- Qualifications: 6+ years in Data Engineering with strong skills in Python and Apache Spark.
The predicted salary is between 70000 - 90000 £ per year.
We are looking for a Data Engineer to design, develop, and maintain robust data pipelines for Deposits and Treasury applications, working with large-scale datasets and enabling financial reporting and analytics use cases.
Responsibilities
- Design, develop, and maintain robust data pipelines for Deposits and Treasury applications.
- Work with large-scale structured and unstructured datasets using Apache Spark / PySpark.
- Develop high-quality, reusable, and efficient code in Python.
- Collaborate with business stakeholders to understand data requirements related to treasury products, liquidity, and deposits.
- Build and optimize ETL/ELT processes for data ingestion, transformation, and integration.
- Support data modelling for financial reporting and analytics use cases.
- Create and maintain data visualizations and dashboards using tools such as Amazon Quicksight, Power BI, Tableau, etc.
- Ensure data quality, governance, and compliance with financial regulations.
- Troubleshoot performance issues and optimize data workflows.
- Work closely with cross-functional teams including analysts, architects, and product owners.
Skills
Must have
- At least 6 years of experience in Data Engineering space.
- Strong experience building and maintaining data pipelines for banking data domains.
- Hands‑on expertise with Apache Spark / PySpark for large-scale data processing.
- Strong Python development skills with emphasis on reusable, efficient code.
- Solid ETL/ELT engineering experience (ingestion, transformation, integration).
- Experience supporting data modelling for reporting/analytics use cases.
- Exposure to BI/dashboarding tools such as Amazon Quicksight, Power BI, Tableau (or similar).
- Practical experience with data quality, governance, and working in regulated environments.
Nice to have
N/A
[DXCSynergy] Data Engineer - Treasury employer: Luxoft
Join a leading financial institution that values innovation and collaboration, offering a dynamic work culture where your contributions as a Data Engineer will directly impact treasury applications and financial reporting. With a strong focus on employee growth, we provide ample opportunities for professional development and skill enhancement, all while working in a vibrant location that fosters creativity and teamwork. Enjoy competitive benefits and the chance to work with cutting-edge technologies in a supportive environment that prioritises data quality and compliance.
StudySmarter Expert Advice🤫
We think this is how you could land [DXCSynergy] Data Engineer - Treasury
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even just grab a coffee with someone who’s already in the data engineering space. You never know when a casual chat could lead to your next big opportunity.
✨Show Off Your Skills
Don’t just tell them what you can do; show them! Create a portfolio of projects that highlight your experience with data pipelines, Apache Spark, and Python. Share it on platforms like GitHub or your personal website to give potential employers a taste of your work.
✨Ace the Interview
Prepare for those interviews by brushing up on common data engineering questions and scenarios. Practice explaining your past projects and how you tackled challenges, especially around ETL processes and data quality. Confidence is key!
✨Apply Through Our Website
Make sure to apply directly through our website! It not only shows your interest but also helps us keep track of your application. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace [DXCSynergy] Data Engineer - Treasury
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Engineer role. Highlight your experience with data pipelines, Apache Spark, and Python. We want to see how your skills match what we're looking for!
Showcase Your Projects:Include specific projects where you've built or maintained data pipelines, especially in banking or financial contexts. This helps us understand your hands-on experience and how you tackle real-world challenges.
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points for your achievements and responsibilities. We appreciate straightforward communication that gets right to the heart of your experience.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Luxoft
✨Know Your Data Pipelines
Make sure you can talk confidently about your experience with data pipelines, especially in the context of banking. Be ready to discuss specific projects where you've designed or maintained these pipelines, and how they relate to treasury applications.
✨Show Off Your Coding Skills
Brush up on your Python skills before the interview. Be prepared to explain your approach to writing reusable and efficient code, and maybe even share examples of your work with Apache Spark or PySpark. They’ll want to see that you can not only code but also optimise performance.
✨Understand the Business Needs
Familiarise yourself with the financial products and data requirements related to treasury and deposits. Being able to articulate how your technical skills can meet these business needs will set you apart from other candidates.
✨Visualisation is Key
Since you'll be working with BI tools like Amazon Quicksight or Power BI, it’s a good idea to have a few examples of dashboards or visualisations you've created. Discuss how these tools helped in decision-making processes and improved data quality.
