At a Glance
- Tasks: Lead a dynamic team to architect and implement cutting-edge data engineering solutions.
- Company: Join JPMorgan Chase, a top global financial institution with a focus on innovation.
- Benefits: Competitive salary, comprehensive benefits, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and career advancement.
- Why this job: Make a significant impact in the financial sector by driving advanced AI and data engineering practices.
- Qualifications: Extensive experience in software development, AI systems, and data engineering required.
The predicted salary is between 72000 - 88000 £ per year.
If you are looking for a game-changing career, working for one of the world's leading financial institutions, you’ve come to the right place. As a Director of Data Engineering at JPMorgan Chase within the Corporate Sector, you provide expertise and engineering excellence as an integral part of an agile data engineering team. To enhance, build, and deliver a trusted market leading Global Know Your Customer (KYC) and Risk Assessment Data Platform 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 technologies to support one or more of the firm’s portfolios. This role is suited to a Senior Director Level Engineer who has hands-on skills to lead across multiple teams—defining architecture, engineering practices and standards, and delivering high-impact software that scales.
Job responsibilities
- Architects and implements complex, scalable engineering frameworks and solutions using modern software design principles.
- Develops secure, high-quality production code for data-intensive applications and platforms, and reviews and mentors other engineers.
- Creates durable, reusable software frameworks and patterns that are leveraged across teams and functions.
- Designs and governs agentic AI systems, including multi-agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environments.
- Establishes engineering standards for LLM-based applications — RAG pipelines, embedding workflows, vector store integrations, and model serving — ensuring safety, observability, and reproducibility at scale.
- Drives adoption of advanced technical methods and practices aligned with the latest industry standards and product development methodologies.
- Serves as the function's go-to subject matter expert in one or more areas of focus within data engineering, platform architecture, or AI systems.
- Advises cross-functional teams on technological matters within your domain of expertise.
- Influences leaders and senior stakeholders across business, product, and technology teams on technical strategy and direction.
- Architects and governs agentic AI-enabled engineering workflows to improve delivery speed, code quality, and operational outcomes at scale.
- Applies knowledge of tools within the Software Development Life Cycle toolchain to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
- Hands-on practical experience delivering system design, application development, testing, and operational stability at enterprise scale.
- Hands-on experience designing and deploying production AI/ML systems, including LLM-based applications and agentic architectures.
- Expert in one or more programming languages, particularly Python and/or Java.
- Advanced knowledge of software application development and technical processes, with considerable depth in one or more disciplines (e.g., cloud, AI/ML, data engineering).
- Experience in large-scale data processing, microservices, API design, Kafka, Redis, MemCached, observability tools, and orchestration frameworks.
- Advanced working knowledge of relational and NoSQL databases, vector stores, data lake architectures, and data governance.
- Practical cloud-native experience (AWS, Azure, or GCP).
- Ability to present and effectively communicate with senior leaders and executives.
- Demonstrable experience designing and leading adoption of agentic AI-enabled development practices across teams.
- Strong understanding of responsible AI use and control expectations in engineering workflows.
Preferred qualifications, capabilities, and skills
- Experience with modern data platforms such as Databricks or Snowflake.
- Deep hands-on experience with Spark/PySpark and other big data processing technologies.
- Expertise in open-source table formats and catalog services such as Apache Iceberg.
- Experience with LLM orchestration frameworks and model serving infrastructure or managed endpoints.
- Familiarity with AI evaluation and observability practices.
- Understanding of agentic design patterns and how to constrain agent autonomy in high-stakes financial workflows.
- Awareness of AI risk and regulatory considerations relevant to AI use in financial decision-making.
Director of Data Engineering in Glasgow 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.
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We think you need these skills to ace Director of Data Engineering 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!
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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!
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✨Brush Up on Your Statistics
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✨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.
