Planning and Analysis Data Modeler – Senior Associate

JPMorgan Chase JPMorgan Chase · Banking · LONDON, United Kingdom · Corporate Sector

This role focuses on designing and implementing data models for financial analysis within a fintech company. While it mentions utilizing AI-assisted tools and exploring embedding AI into workflows, the core responsibilities revolve around traditional data modeling, SQL/Python for data engineering, and ETL/ELT processes, rather than building or shipping AI/ML models directly.

What you'd actually do

  1. Design, develop, test, and refine data models and analytic prototypes
  2. Solve complex data challenges at the intersection of finance and technology
  3. Support the development of cloud-based data lakehouse solutions for Planning & Analysis
  4. Guide and support data consumers in leveraging data products for analytics and planning
  5. Utilize AI-assisted coding tools to accelerate development and optimize SQL

Skills

Required

  • Strong analytical and problem-solving skills with attention to detail
  • Hands-on experience building and prototyping data models to address user needs
  • Proficient in SQL and Python for data analysis, engineering, and transformation
  • Relevant experience developing, testing, and refining data models using dimensional and relational approaches
  • Curious mindset with a drive for continuous improvement
  • Familiarity with prompt engineering principles and LLM-based tools
  • Effective communication skills to present data findings and support users
  • Experience with ETL/ELT processes and architecture in data pipelines
  • Experience building models for dashboard or cube consumption
  • Familiarity 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

Nice to have

  • Experience with Databricks and notebook-based development
  • Experience working with financial and workforce datasets

What the JD emphasized

  • design and implement end-to-end data models
  • build and refine data models
  • improving data pipelines
  • building models for dashboard or cube consumption