At a Glance
- Tasks: Lead innovative data engineering projects and design scalable data warehouse solutions.
- Company: Join JPMorgan Chase, a leader in financial services with a commitment to innovation.
- Benefits: Competitive salary, comprehensive benefits, and opportunities for professional growth.
- Other info: Collaborative culture that values diversity, inclusion, and continuous learning.
- Why this job: Make a significant impact in a dynamic environment while working with cutting-edge technologies.
- Qualifications: 8+ years in data engineering with expertise in large-scale data solutions.
The predicted salary is between 51750 - 63250 £ per year.
As a vital core member of an agile team, your innovative spirit and unparalleled knowledge is a catalyst for change and serves as an inspiration for bringing innovative solutions to life. Your visionary leadership and pursuit of excellence makes a lasting impact on the firm and industry. As a Principal Data Engineer at JPMorganChase, you provide expertise and data engineering excellence as an integral part of an agile team to enhance, build, and deliver data collection, storage, access, and analytic solutions in a secure, stable, and scalable way. Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across various data pipelines and data architectures to support one or more of the firm’s portfolios.
Job responsibilities
- Proactively partners with the principle architect to transform design intent into a practical delivery roadmap, ensuring short, critical deliverables are met in a scalable, strategic program.
- Designs, builds, and optimizes complex data warehouse solutions, ensuring scalability, reliability, and performance through hands-on engineering and advanced technical expertise.
- Develops and implements robust data pipelines and ETL processes, directly contributing to the team’s delivery of secure and stable data architectures.
- Applies advanced data modeling techniques, including linear algebra, statistics, and geometrical algorithms to solve real world business challenges and drive actionable insights.
- Collaborates closely with cross-functional teams to integrate data solutions into business-critical applications, actively participating in code reviews and architectural decisions.
- Evaluates and selects data visualization tools, and creates impactful visualizations to communicate key trends and insights to stakeholders.
- Provides technical mentorship to junior engineers, sharing best practices and fostering a culture of continuous learning through hands-on guidance.
- Champions the firm’s culture of diversity, opportunity, inclusion, and respect, modeling these values in daily interactions and project work.
Required qualifications, capabilities, and skills
- Minimum 8 years of hands-on experience in data engineering, designing, building, and delivering large-scale data warehouse solutions in enterprise environments.
- Experience applying expertise and new methods to determine solutions for complex technology problems in one or more technical disciplines.
- Proven track record of leading end-to-end delivery of data pipelines, ETL processes, and data architecture projects, directly contributing to successful outcomes.
- Experience leading a product as a Principle Engineer or Technical Delivery Lead.
- Ability to present and effectively communicate technical concepts, project progress, and solution impacts to Senior Leaders and Executives.
- Demonstrated experience in the domain of emerging technologies and their best practices, with a focus on practical implementation and integration.
- Shows a proficient understanding of existing data management systems and continuous learning to understand new data management systems.
- Strong proficiency in hands-on development using industry-standard tools and languages (e.g., SQL, Python, Spark, cloud platforms), with a commitment to code quality and delivery excellence.
Preferred qualifications, capabilities, and skills
- Hands-on experience with Databricks for scalable data engineering, analytics, and collaborative development, as well as proficiency in managing large datasets using Apache Iceberg for efficient, reliable table management and data lake optimization.
- Experience architecting and implementing enterprise-scale data warehouses on cloud platforms (e.g., AWS, Azure, GCP), including migration from legacy systems.
- Advanced proficiency in designing and optimizing data models for analytics, reporting, and business intelligence use cases.
- Demonstrated ability to automate data pipeline deployment and monitoring using CI/CD tools and infrastructure-as-code practices.
- Hands-on expertise with distributed data processing frameworks (e.g., Apache Spark, Hadoop) and real-time data streaming technologies (e.g., Kafka, Kinesis, Spark).
- Track record of collaborating with cross-functional teams (engineering, analytics, business stakeholders) to deliver impactful data solutions.
- Experience evaluating, selecting, and integrating emerging data technologies and tools to improve scalability, security, and performance.
- Published contributions to open-source projects, technical blogs, or industry forums related to data engineering.
- Advanced degree in Computer Science, Engineering, Mathematics, or a related field.
- Recognized for mentoring and developing technical talent, fostering a culture of innovation and continuous improvement.
Principle Data Warehouse Engineer (Executive Director) in London employer: JPMorganChase
JPMorganChase is an exceptional employer, offering a dynamic work environment in Greater London where innovation thrives. With a strong commitment to diversity and inclusion, employees benefit from collaborative agile teams, extensive professional development opportunities, and the chance to work on cutting-edge technology products that shape the future of finance. Join us to be part of a culture that values your contributions and supports your growth.
StudySmarter Expert Advice🤫
We think this is how you could land Principle Data Warehouse Engineer (Executive Director) in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like JPMorganChase!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Principle Data Warehouse Engineer (Executive Director) at JPMorganChase.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like JPMorganChase.
✨Apply Directly through Our Website
When you find a suitable opening like Principle Data Warehouse Engineer (Executive Director) at JPMorganChase, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Principle Data Warehouse Engineer (Executive Director) in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at JPMorganChase, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at JPMorganChase. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at JPMorganChase
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at JPMorganChase!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
