Data Domain Modeler – Vice President

JPMorgan Chase & Co.
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist (Full Stack)

Senior DataOps Engineer

Data Scientist

Data Scientist KTP Associate

Senior Data Scientist

Data Scientist Lead

Information Architecture within Corporate Financial Analysis (FA) is working to build scalable, end-to-end data products that enable centralized, self-service data sourcing through an array of consumption patterns optimized for Planning and Analysis (P&A) functions.

As a Data Domain Modeler in Transformation & Innovation team you will lead the design and implementation of end-to-end data models starting from raw data to the semantic layer that makes our data more accessible and understandable for different persona ranging from: finance users, data analysts, automation, quantitative research and machine learning teams. Being part of an influential and data-centric team focused on data accessibility you will work on designing new data models for domains such as headcount, contractors, financials, forecasting models, markets, and macro-economic scenarios. You will also represent the data domains in the overall information architecture strategy to optimize data models for end user consumption, identify data homogenization opportunities, and optimize data pipelines in our data lake-house.

You will lead the engagement and partner with product owners, business users (both technical and non-technical), data providers, and technology teams across the entire finance function to design and deliver data products.

Job Responsibilities

Work on some of the most complex and highly visible data problems in finance, at the intersection of finance and technology Design and build new cloud based data lakehouse for the P&A community, leveraged by Analysts to CFO for their day to day reporting Work on wide range of data sets and use case to support different Planning & Analysis processes, and personally lead and drive the design of them Create solutions for key data challenges and implements innovative technology-based solutions at the bank such as enterprise data catalog, and AI-enabled conversational analytics Partner with other high-performing teams within JPM to inspire innovation and champion change throughout the bank 

Required qualifications, capabilities, and skills

Strong analytical and problem solving skills with attention to details to formulate effective data models to address users consumption pain points, and to lead their delivery Curious mind to dig deep into the business and data to understand the context: Inquisitive and analytical mindset, challenges the status quo, and strive for excellence 5+ years of relevant experience designing and implementing data models and analytic solutions using dimensional and relational data models Hands-on and flexible approach to creating solutions aligned to the tools and skills of the client user. Strong communication skills to present data products and educate data consumers Experience using programming languages (SQL & Python) for data analysis, data engineering, and transformation to answer business questions  Experience building analytics dashboard or building models suited for interactive dashboard consumption Experience with ETL / ELT process and architecture to move data across pipelines in a lake Experience with cloud-based data lake platforms such as AWS, Azure or Google Cloud Bachelor’s degree in computer science, data science, information systems, business analytics, or related discipline

Preferred qualifications, capabilities, and skills

Experience with Databricks

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.