Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer Director

JPMorgan Chase & Co.
City of London
1 week ago
Create job alert
Overview

We are seeking an experienced Data Engineer to lead the development of a centralized Communications database. This role will be instrumental in aggregating, modeling, and visualizing data from both internal and external communications channels, including third-party agencies and tools. The successful candidate will partner with technology teams and external agencies to design robust data models, build scalable data pipelines, and deliver actionable insights through advanced BI and AI-driven visualizations. This role supports communications teams across the firm, including CCB Communications, US regional Communications, CIB Communications, Firmwide Impact and more.

Key Responsibilities
  • Database Architecture & Data Modeling: Design and implement relational data models to support the aggregation and analysis of communications data from diverse sources (internal channels, external agencies, third-party tools).
  • Data Integration & Pipeline Development: Build and optimize data pipelines for ingesting, transforming, and centralizing communications data, ensuring data quality and consistency.
  • BI & Visualization Solutions: Develop and manage advanced BI solutions (e.g., Tableau, ThoughtSpot) to visualize the impact and outcomes of communications efforts, enabling data-driven decision-making.
  • AI & Advanced Analytics: Collaborate with data scientists and analytics teams to deploy machine learning models and AI solutions that measure and predict communications effectiveness.
  • Cross-Functional Collaboration: Work closely with communications teams across CCB, Corporate, CIB, Corporate Impact, and Employee Experience, as well as external agencies and technology partners, to understand data needs and deliver tailored solutions.
  • Process Optimization: Identify and implement process improvements, automate manual workflows, and redesign infrastructure for scalability and performance.
  • Documentation & Compliance: Document data models, metadata, and machine learning processes to ensure transparency, compliance, and knowledge sharing.
Required Qualifications, Capabilities, and Skills
  • Proven experience in data engineering, data modeling, and database architecture.
  • Hands-on expertise in BI platforms and tools (e.g., Tableau, ThoughtSpot) for advanced analytics and data visualization.
  • Proficiency in Alteryx, SQL, and Python for data integration, transformation, and analysis.
  • Experience with cloud platforms (Databricks, AWS, Azure) and deploying/managing machine learning models in production.
  • Strong understanding of MLOps and building automated pipelines for model deployment and monitoring.
  • Demonstrated ability to collect, refine, and transform data from diverse sources, including third-party tools and external agencies.
  • Excellent analytical, problem-solving, and communication skills.
  • Experience working with cross-functional teams in a dynamic, fast-paced environment.
  • Mastery of SQL, including designing and optimizing complex queries and database structures.
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

J.P. Morgan's Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Engineer Director

Senior Data Engineer x2

Sr Data Engineer (hybrid working)

Lead Data Engineer

Data Engineer

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.