Executive Director – Head of Asset Management Operations, Data Strategy

J.P. Morgan
London
1 month ago
Applications closed

Related Jobs

View all jobs

Head of Data Strategy

Data Analytics Manager - Heartwood Collection

Senior Data Analyst

Senior Data Analyst

Group Head of Data - Enterprise Data Strategy - Microsoft Fabric - Permanent - London

Data Quality Officer

Shape the future of Asset Management with visionary data leadership. As the Executive Director and Head of Asset Management (AM) Operations, Data Strategy, you’ll shape and execute a cutting‑edge approach to data development, governance, and delivery—empowering business growth, operational excellence, and advanced analytics.


In this role, you will leverage your expertise infund accounting, investment accounting, and accounting principlesto ensure data integrity and compliance across all operational processes. You will ensure that data inputs, outputs, and impacts are well understood and optimized for key consumers, including portfolio managers, investment specialists, client service teams, and other operations stakeholders.


Join us today to transform data into a strategic asset at the heart of our operations!


Job Responsibilities

  • Partner with the Asset & Wealth Management (AWM) Chief Data Officer to define target data architecture and maintain scope for position, transaction, performance, and attribution data domains, ensuring alignment with Asset Management’s data strategy.
  • Collaborate with business process owners and Technology architects to design scalable, flexible data architecture that meets business and accounting requirements.
  • Own the data landscape for these domains, including migration planning from legacy to strategic systems of record.
  • Identify and govern Critical Data Elements (CDEs), ensuring regulatory compliance, data lineage, and adherence to accounting standards.
  • Manage all data domain artifacts (data dictionary, quality rules, lineage documentation) and lead a team of AM Operations Data Owners for their maintenance and enhancement.
  • Govern and evolve the data domain structure through participation in the Data Architecture Council and decision‑making on domain changes.
  • Promote data literacy and a data‑driven culture across domains and all key Product, Operations, and Technology partners while ensuring robust data management practices and operating models in partnership with AM Operations leadership.
  • Collaborate with other Data Owners to ensure data integration, integrity, secure access, and enforcement of domain boundaries, especially for accounting and performance data.
  • Own and enforce data contracts, ensuring clear standards for data quality, accessibility, and usage between producers and consumers.
  • Lead engagement with data producers, consumers, and reporting/BI/data science communities to understand requirements, prioritize initiatives, and drive adoption of data products and enhancements.
  • Oversee data risk metrics, compliance, and governance through participation in relevant councils, and direct Product Owners to uplift data quality, manage retention/destruction, and ensure security, confidentiality, and regulatory compliance.

Required qualifications, capabilities, and skills

  • Bachelor’s degree with demonstrable industry experience in a data‑related role, with experience infund accounting, investment accounting, and accounting principles.
  • Subject matter expertise in position, transaction, performance, and attribution data domains within an Asset Management ecosystem.
  • Experience managing delivery across multiple workstreams with varying timelines, priorities, and complexities, especially in accounting, performance, and operations environments.
  • Demonstrated ability to manage tight delivery timelines and ensure the product and organization are on track to execute and deliver strategic changes that meet goals.
  • Ability to execute via successful internal partnerships with other organizations, with the ability to influence people at all levels across a broad variety of job functions.
  • Excellent leadership skills in managing products, programs, projects, and teams.
  • Structured thinker and effective communicator with excellent written communication skills.
  • Ability to crisply articulate complex technical, performance, and accounting concepts simply to senior audiences with poise and confidence.
  • Technical understanding of data management and governance, cloud‑based data platforms, or data architecture.
  • Understanding of product development and Agile methodologies, with experience in product management focused on data products and data‑driven decision‑making.

Preferred qualifications, capabilities, and skills

  • Strong familiarity with data management tooling (e.g., quality, observability, discovery, profiling).
  • Experience with regulatory reporting and compliance in Asset Management Operations.
  • Experience supporting data needs and impacts for portfolio managers, investment specialists, client service, and operations stakeholders.
  • Strong familiarity with advanced analytics, machine learning, and AI applications in a business context.
  • Demonstrated experience with cloud‑based data platforms and technologies (e.g., AWS, Azure, Google Cloud).


#J-18808-Ljbffr

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.