At a Glance
- Tasks: Drive data innovation and collaborate on impactful AI and analytics solutions.
- Company: Join JPMorganChase, a leader in financial services with a diverse culture.
- Benefits: Competitive salary, growth opportunities, and a supportive learning environment.
- Other info: Dynamic team environment with excellent career advancement potential.
- Why this job: Make a lasting impact while working with cutting-edge technologies and talented colleagues.
- Qualifications: Experience in data engineering, Python, and AI-assisted development tools.
The predicted salary is between 56700 - 69300 £ per year.
Join us as a Data Engineer and help drive data innovation at JPMorganChase. You will collaborate with talented colleagues to deliver impactful solutions that power AI and analytics initiatives across the firm. We value your expertise, encourage growth, and support your journey to make a lasting impact. Experience a culture that celebrates diverse perspectives and fosters continuous learning.
As a Data Engineer in the Corporate Technology team supporting CIO, Treasury & Corporate Risk Management, you will enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You will maintain critical data pipelines and architectures across multiple technical areas, supporting the firm’s business objectives. Your work will drive innovation, operational excellence, and team success, contributing to a collaborative culture that values your ideas and technical skills.
Job Responsibilities:
- Make data available for AI and analytics initiatives, working closely with use case owners to define requirements, manage product dependencies, and support agile routines that oversee cross-product data dependencies and prioritize delivery.
- Collaborate with business, technology, and operations partners to understand data requests and accelerate provisioning through deployment of "AI for Data".
- Drive adoption of AI-assisted development tools (e.g., Claude Code, Copilot) to accelerate delivery and improve developer productivity.
- Partner with business and technology teams to rapidly prototype and deploy analytics and tooling, leveraging AI/ML and innovative approaches.
- Implement and manage platform controls, including access, security, and compliance, ensuring all data and AI solutions meet firmwide SDLC standards.
- Identify the lineage and provenance of critical data assets to support governance, regulatory, and business requirements; embed evergreen controls on data flows to improve safety, transparency, and traceability.
- Drive insight into areas of efficiency and risk through consolidation and reengineering of data flows.
- Develop proactive controls to reduce the time from data quality issue identification to resolution, improving client experience and driving operational efficiency.
- Demonstrate control environment improvements and reduction in toil through common tooling and frameworks; uplift the metadata (semantic layer) of existing data to support AI and Natural Language Query (NLQ) usage, accelerate adoption of Mesh data architecture, reduce consumer friction, and deliver data product prototypes.
- Use enterprise-authorized AI capabilities within the work environment to accelerate data platform and model design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
- Apply reuse-first, AI-assisted practices within delivery and operational routines (e.g., backup/recovery validation and access control review support), ensuring traceability/auditability and alignment to resiliency and security expectations.
Required Qualifications, Capabilities, and Skills:
- Formal training or certification on software engineering concepts.
- Experience and awareness in working within Risk Analytics space; preferred knowledge of corporate bond investment assets and structured credit products such as securitisation (e.g., CLO, CMBS, RMBS, ABS).
- Proven experience integrating AI-assisted development tools (e.g., Claude Code, Copilot, or similar) into engineering workflows.
- Experience in strategic or transformational change initiatives, including data governance, data quality, or analytics transformation programs.
- Strong technical skills in data profiling, analysis, and data management using modern tools and environments (Python, R, SQL, Spark, DataBricks, cloud platforms).
- Understanding of data lineage concepts and experience with lineage analysis, metadata management, and data cataloguing.
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
- Ability to review and validate AI-assisted outputs (e.g., code, model/design summaries or operational checklists) before use, escalating when uncertain and following data handling requirements.
- Hands-on practical experience delivering system design, application development, testing, and operational stability.
Preferred Qualifications, Capabilities, and Skills:
- Hands-on experience with data lineage tools and techniques, including graph & vector databases and metadata management platforms.
- Hands-on experience with LLM Ops, MLOps, and AI/ML platform deployment at scale.
- Familiarity with business-led analytics delivery models and rapid prototyping frameworks.
- Experience with AI/ML governance, prompt engineering, and integrating AI tools into SDLC.
- Experience with AI/ML technologies and their application to data management challenges (e.g., automated data profiling, metadata enrichment).
- Understanding of agile and product management methodologies and experience working in agile teams.
- Excellent interpersonal skills and ability to build strong working relationships with business, technology, and control stakeholders across global teams.
Data Engineer III - Python, Databricks, React in Glasgow employer: Jpmorgan Chase & Co.
JPMorgan Chase & Co. is an exceptional employer, offering a dynamic work environment in Bournemouth where innovation and collaboration thrive. Employees benefit from a strong focus on professional development, inclusive team culture, and the opportunity to work with cutting-edge technology in a supportive atmosphere that values continuous improvement and engineering excellence.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer III - Python, Databricks, React in Glasgow
✨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 Jpmorgan Chase & Co.!
✨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 Data Engineer III - Python, Databricks, React at Jpmorgan Chase & Co..
✨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 Jpmorgan Chase & Co..
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer III - Python, Databricks, React at Jpmorgan Chase & Co., 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 Data Engineer III - Python, Databricks, React in Glasgow
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 Jpmorgan Chase & Co., 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 Jpmorgan Chase & Co.. 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 Jpmorgan Chase & Co.
✨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 Jpmorgan Chase & Co.!
✨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.
