Transformation and Innovation Team - Analytics Solutions Senior Associate

JPMorganChase • Jersey City, NJ

1d

About The Position

Join a team that delivers innovative products and solutions that enable advanced use of data and technology to drive process optimization and enhanced analytics for Finance, P&A, and Business Management. As an Analytics Solutions Senior Associate, you will build AI-ready analytics datasets and semantic models, with a strong emphasis on Databricks lakehouse engineering as the foundation for Finance reporting and planning use cases. The role supports a strategic migration from Oracle Database / Essbase to a modern stack using Databricks for curated, governed data products and Atoti for semantic/cube analytics.

Requirements

• Bachelor’s degree required (analytics, finance, engineering or related field), or equivalent experience.
• 4+ years of experience in data modeling, analytics engineering, data platform transformation, or Finance analytics roles (financial services preferred).
• Hands-on experience building curated datasets using Databricks (or equivalent lakehouse platform), including strong SQL and data transformation skills and an understanding of performance/cost tradeoffs.
• Experience designing semantic models for enterprise analytics (dimensions, measures, hierarchies, aggregation behavior) and partnering with BI/consumption teams.
• Working knowledge of GenAI integration patterns for analytics (e.g., natural-language-to-metrics, grounded responses via semantic layers) and how metadata quality impacts outcomes.
• Ability to translate legacy logic (e.g., Essbase/Oracle) into modern curated-layer and semantic-layer implementations, including reconciliation and validation.
• Strong analytical, independent problem-solving, and communication skills; able to partner effectively across Product, Technology and Finance stakeholders.
• High attention to detail, ownership mindset, and ability to manage multiple priorities in a fast-paced environment.

Nice To Haves

• Experience with Atoti (or similar OLAP/semantic tooling) and understanding of cube design/performance considerations.
• Familiarity with Excel-based consumption patterns and add-ins (including AnaplanXL).
• Hands-on familiarity with Model Context Protocol (MCP) concepts and patterns (tool/schema exposure, semantic discovery, guardrails), especially in the context of analytics/semantic layers.
• Exposure to data quality rules, lineage documentation, access controls/entitlements, and audit/control expectations in Finance.
• Agile delivery experience and familiarity with Jira/Confluence.

Responsibilities

• Design and deliver curated analytics datasets in Databricks (conformed dimensions, metric-ready fact tables, standardized grains) that serve as the authoritative foundation for Finance semantic models and downstream consumption.
• Develop and optimize transformation logic and pipelines in Databricks (e.g., incremental processing patterns, performance tuning, cost-conscious compute usage), partnering with Technology while owning the data/modeling requirements and validation.
• Translate Databricks curated datasets into Atoti semantic/cube models (dimensions, hierarchies, measures, aggregation logic) and ensure performance and usability for Finance personas.
• Create and maintain structured semantic metadata (business definitions, synonyms, calculation narratives, grain constraints, permitted aggregations, known limitations) to improve GenAI grounding and reduce ambiguity/hallucination risk in natural-language analytics.
• Convert Essbase/Oracle logic into lakehouse and semantic-layer constructs, documenting mapping rules, assumptions, and gaps; support parallel runs and model validation.
• Ensure curated data + semantic models support Excel workflows via AnaplanXL, including drill paths, hierarchies, measure behavior, and user-facing definitions.
• Contribute to cutover readiness, issue triage, adoption metrics, and decommissioning of legacy Essbase/Oracle-dependent reporting by ensuring Databricks datasets and semantic models meet functional and performance requirements.
• Establish reusable Databricks patterns (data quality checks, validation harnesses, reconciliation templates) and contribute to playbooks for Finance lakehouse and semantic modeling.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Number of Employees: 5,001-10,000 employees