Lead Software Engineer

J.P. Morgan
Glasgow
2 months ago
Applications closed

Related Jobs

View all jobs

Powertrain Software Engineer

Senior Software/Data Engineering Lead- Global Investment Bank | London, UK

Lead Electronics Engineer

Senior Software Engineer

Research Software Engineer

Lead Data Engineer

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the Data Core Engineering Team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job Responsibilities

  1. Executes creative software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
  2. Develops secure high-quality production code, and reviews and debugs code written by others.
  3. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
  4. Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture.
  5. Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies and coordinate with engineering and infrastructure teams to ensure the self-serve data platform supports data catalog.
  6. Leads implementation and adoption of data product specific metadata standards stored in the catalog across the organization.
  7. Designs and delivers training programs for maintaining the data product for managers and other stakeholders aligned to data mesh principles and practices to engage with data consumers to assess satisfaction and gather requirements for data product findability, accessibility, interoperability, and reuse in the catalog.
  8. Adds to team culture of diversity, equity, inclusion, and respect.

Required Qualifications, Capabilities, and Skills

  1. Formal training or certification on software engineering concepts and proficient applied experience.
  2. Hands-on practical experience delivering system design, application development, testing, and operational stability.
  3. Advanced in one or more programming language(s) Java or Python.
  4. Proficiency in automation and continuous delivery methods.
  5. Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
  6. Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.).
  7. Extensive experience in data management, analytics, and data architecture in financial services.
  8. Experience in cataloging and managing data products, with a good understanding of Domain Driven Design (DDD), federated automated governance, and metadata management to support data-intensive use cases, emphasizing the integration of source-aligned data products to enhance the organization's data mesh.
  9. Experience in supporting cross-domain organizational structure refinement, data product interoperability, and the establishment of federated ownership and governance through strategic metadata management and understanding of data mesh principles and their practical application.
  10. Hands-on experience with data catalog platforms, preferably Atlan, Databricks and constructs like asset bundles and data contracts.
  11. Advanced knowledge of data governance, risk management, and regulatory compliance in financial services.

Preferred Qualifications, Capabilities, and Skills

  1. Advanced degree in Computer Science, Data Science, or related field.
  2. Certifications in data management or relevant technologies.
  3. Experience with agile methodologies and DevOps practices.
  4. Knowledge of data ontologies and industry standards for cataloging data products.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.