At a Glance
- Tasks: Build and maintain scalable data platforms for mortgage systems using cutting-edge technologies.
- 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 governance.
- Why this job: Make a real impact in the mortgage industry while working with advanced data technologies.
- Qualifications: 6+ years in Data Engineering, strong skills in Apache Spark and Python.
The predicted salary is between 60000 - 80000 £ per year.
We are looking for a Data Engineer with strong hands-on expertise in building scalable data platforms to support mortgage and lending systems. The role involves working on loan origination, servicing, and risk analytics data, enabling reporting, regulatory compliance, and business insights, using technologies including Apache Spark, Python, and BI tools such as Amazon QuickSight.
Responsibilities
- Design, build, and maintain data pipelines supporting mortgage systems, including origination, underwriting, and servicing.
- Process large-scale datasets using Apache Spark, with a preference for PySpark.
- Develop clean, scalable, and efficient Python code.
- Integrate data from multiple sources such as loan systems, credit bureaus, and third-party providers.
- Build and optimize ETL/ELT workflows for batch and near real-time processing.
- Develop datasets and dashboards using Amazon QuickSight or comparable tools for mortgage reporting and key performance indicators.
- Support regulatory and compliance reporting related to loan performance and risk exposure.
- Ensure data quality, lineage, and governance across mortgage data platforms.
- Collaborate with business stakeholders in risk, underwriting, and operations to translate requirements into data solutions.
- Optimize the performance and scalability of data pipelines and storage systems.
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
Data Engineer - Mortgages employer: Luxoft
Join a forward-thinking company that values innovation and collaboration, where as a Data Engineer in the Mortgages sector, you will play a crucial role in shaping scalable data platforms. Our supportive work culture fosters continuous learning and professional growth, offering opportunities to work with cutting-edge technologies like Apache Spark and Amazon QuickSight. Located in a vibrant area, we provide a dynamic environment that encourages creativity and teamwork, making us an excellent employer for those seeking meaningful and rewarding careers.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - Mortgages
✨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 refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those involving Apache Spark and Python. This gives you a chance to demonstrate your hands-on expertise and makes you stand out from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with ETL/ELT workflows and how you've tackled challenges in past projects. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! We love seeing applications come directly from candidates who are genuinely interested in joining our team. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Data Engineer - Mortgages
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Engineer role. Highlight your experience with Apache Spark, Python, and any relevant banking data domains. We want to see how your skills match what we're looking for!
Showcase Your Projects:Include specific projects where you've built scalable data platforms or worked on ETL/ELT workflows. This gives us a clear picture of your hands-on expertise and how you can contribute to our mortgage systems.
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points for your achievements and responsibilities to make it easy for us to read. We appreciate straightforward communication!
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. We can't wait to see what you bring to the table!
How to prepare for a job interview at Luxoft
✨Know Your Tech Inside Out
Make sure you’re well-versed in Apache Spark, PySpark, and Python. Brush up on your coding skills and be ready to discuss how you've built and maintained data pipelines in the past. They’ll likely ask for specific examples, so have a few projects in mind that showcase your expertise.
✨Understand the Mortgage Landscape
Familiarise yourself with mortgage systems, loan origination, and risk analytics. Knowing the ins and outs of these areas will help you connect your technical skills to the business needs. Be prepared to discuss how your work can impact regulatory compliance and business insights.
✨Showcase Your Problem-Solving Skills
Prepare to tackle hypothetical scenarios or case studies during the interview. Think about how you would approach building ETL/ELT workflows or optimising data pipelines. Demonstrating your thought process and problem-solving abilities will impress the interviewers.
✨Ask Insightful Questions
Don’t forget that interviews are a two-way street! Prepare thoughtful questions about the team’s current projects, challenges they face, and how they measure success. This shows your genuine interest in the role and helps you gauge if it’s the right fit for you.
