Analytics Solutions Associate Senior - Dart

JPMorgan Chase JPMorgan Chase · Banking · Plano, TX +1 · Consumer & Community Banking

This role focuses on delivering data-driven solutions and building automated MIS capabilities within JPMorgan Chase's DART team. Responsibilities include end-to-end MIS solution delivery, designing and building ELT/ETL pipelines, creating dashboards, applying advanced analytics (forecasting, anomaly detection), implementing data quality frameworks, and adhering to data governance standards. The role requires strong SQL and Python skills, experience with cloud environments and data visualization tools, and working knowledge of machine learning for operations use cases.

What you'd actually do

  1. Own end-to-end MIS solution delivery: requirements gathering, metric definition, source data acquisition, modeling, transformation, validation, and visualization.
  2. Design and build reliable ELT/ETL pipelines in Python/SQL; implement orchestration, version control, and CI/CD to ensure repeatability and resilience.
  3. Create executive-ready dashboards and self-service data marts (e.g., Tableau) with intuitive UX and clear metric definitions.
  4. Apply advanced analytics (forecasting/time series, anomaly detection, segmentation, queuing/capacity planning) to optimize operational performance.
  5. Implement data quality frameworks (unit/integration tests, validation checks, anomaly monitoring), define SLAs/SLOs, and maintain runbooks.

Skills

Required

  • 5+ years of hands-on analytics experience
  • Bachelor’s degree in a quantitative or technical field
  • Expert-level SQL
  • Strong Python (pandas, NumPy; unit testing with pytest; structured logging; packaging)
  • Automated data pipelines
  • Data lake/cloud environments (Snowflake; AWS services such as S3, Glue, Lambda; or equivalent)
  • Data visualization (Tableau or equivalent)
  • KPI design
  • Dashboard UX
  • Audience-specific storytelling
  • Machine learning for operations use cases (time series forecasting, supervised/unsupervised methods, feature engineering)
  • Data wrangling tools (e.g., Alteryx)
  • R for statistical analysis
  • Excellent verbal and written communication skills
  • Synthesize complex analyses into concise executive narratives and visuals
  • Collaborate across functions and levels
  • Influence decisions
  • Drive adoption of data solutions

Nice to have

  • Preferred experience supporting more than one CCB Operations Function/Line of Business

What the JD emphasized

  • 5+ years of hands-on analytics experience delivering measurable business improvements, with a strong track record in operations analytics or MIS within complex environments.
  • Expert-level SQL (complex joins, window functions, CTEs, performance tuning) and strong Python (pandas, NumPy; unit testing with pytest; structured logging; packaging).
  • Proven experience building automated data pipelines and operating in data lake/cloud environments (Snowflake; AWS services such as S3, Glue, Lambda; or equivalent).
  • Strong data visualization experience (Tableau or equivalent), including KPI design, dashboard UX, and audience-specific storytelling.
  • Working knowledge of machine learning and applied statistics for operations use cases (time series forecasting, supervised/unsupervised methods, feature engineering).