Data Engineer III - Python, Databricks, React

JPMorgan Chase & Co.; Glasgow; On-site; GBP 70,000 - 90,000; Full time

Job summary

JPMorganChase is seeking a Data Engineer in the Corporate Technology team to build and deliver scalable data pipelines and analytics solutions that power AI initiatives across the firm.

You will maintain critical data architectures, collaborate with global teams, and drive improvements in data quality, governance, and operational stability while aligning with SDLC standards and security requirements.

Qualifications

• Formal training or certification on software engineering concepts.
• Experience in Risk Analytics space; knowledge of corporate bonds and securitisation products.
• Proven experience integrating AI-assisted development tools into engineering workflows.
• Experience with data governance, data quality, or analytics transformation programs.
• Strong data profiling, analysis, and management using Python, R, SQL, Spark, DataBricks, and cloud platforms.
• Understanding of data lineage concepts and experience with metadata management and data cataloguing.
• Demonstrated experience using enterprise AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
• Hands-on experience delivering system design, application development, testing, and operational stability.

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 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 of existing data to support AI and NLQ usage, accelerate mesh data architecture adoption.
• Uses 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.
• Applies reuse-first, AI-assisted practices within delivery and operational routines, ensuring traceability/auditability and alignment to resiliency and security expectations.

Skills

Software engineering concepts

Risk Analytics

AI-assisted development tools

Data governance

Python

R

SQL

Spark

DataBricks

Cloud platforms

Data lineage

Metadata management

AI outputs validation

System design

Tools

Claude Code

Copilot

Graph databases

Vector databases

Metadata management platforms

Data cataloguing tools

LLM Ops

MLOps

Job description

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.

Job Summary:
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
• Uses 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
• Applies 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