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

Apply Now

Data Engineer Director

J.P. Morgan
City of London
4 days 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.


#J-18808-Ljbffr

Related Jobs

View all jobs

Director of Data Engineering - Communications Data Solutions

Senior Data Engineer

Data Engineer

Data Engineer

Senior Data Engineer

Senior Associate Director, IT, Data Engineering Technical Lead (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.

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

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.