Lead Data Engineer - Compute Data Platform

Fairygodboss
Glasgow
1 week ago
Create job alert

Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.

As a Lead Data Engineer at JPMorganChase within the Compute Infrastructure Platforms organisation you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm's business objectives.

Job responsibilities
  • Generates data models for their team using firmwide tooling, statistics, and contextual analysis
  • Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way
  • Implements database back-up, recovery, and archiving strategy
  • Evaluates and reports on access control processes to determine effectiveness of data asset security with minimal supervision
  • Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
  • Five years of relevant working experience with both relational and NoSQL databases
  • Experience and proficiency across the data lifecycle
  • Experience with database back-up, recovery, and archiving strategy
  • Experience architecting and managing data solutions on major cloud platforms (AWS, Azure, Google Cloud).
  • Demonstrated ability to implement and oversee data governance frameworks, including regulatory compliance.
  • Hands‑on experience designing, building, and optimizing complex ETL/ELT pipelines for large‑scale, distributed data systems.
Preferred qualifications, capabilities, and skills
  • Proven track record in database performance optimization, including query tuning, indexing strategies, and resource management for both relational and NoSQL systems.
  • Experience leading technical teams, mentoring junior engineers, and fostering collaborative, inclusive environments.
  • Ability to work closely with business stakeholders, data scientists, and software engineers to deliver integrated data solutions.
  • Strong skills in documenting data models, architecture decisions, and operational procedures for knowledge sharing and compliance.
  • Familiarity with CI/CD pipelines, automated testing frameworks, and monitoring tools relevant to data engineering.
  • Familiarity with modern enterprise level compute infrastructure including virtualised and cloud solutions.
  • Familiarity with Databricks, Parquet, Iceberg and, or other high volume solutions.
About us

J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first‑class business in a first‑class way approach to serving clients drives everything we do. We strive to build trusted, long‑term partnerships to help our clients achieve their business objectives.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

About the Team

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.


#J-18808-Ljbffr

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.