Data Analytics Engineer Senior Associate

JPMorganChases Commercial and Investment Bank Finance and Business Management team is looking for a strategic analytical and energetic professional to support the team and partner with the business and help achieve their goals.

As a Data Analytics Engineer - Senior Associate within the Commercial and Investment Bank Finance and Business Management team you will build analytics-ready data models and a trusted semantic layer that standardizes business metrics. You will partner with stakeholders to translate requirements into well-modeled datasets in Databricks/Snowflake using SQL (primary) Python ETL and strong data modeling semantic layer practices. This role is geared toward analytics enablement: designing curated data products defining consistent metrics and enabling scalable self-service reporting. Youll work closely with analytics product and engineering partners to turn business questions into governed reusable models and semantic definitions. You will own the structure and usability of downstream analytics - defining grains dimensions facts conformed entities and metric logic - so teams can move faster with confidence. You will also collaborate with upstream data engineering to ensure source-to-model alignment and ensure data quality and documentation meet a high bar. The successful candidate will bring consistent KPI definitions across dashboards clear semantic conventions performant and well-documented models and a data ecosystem where consumers trust and reuse whats been built.

Job Responsibilities

• Lead development of analytics data models (dimensional and/or domain-oriented) optimized for reporting BI and self-service consumption.
• Design and maintain a semantic layer (standardized metrics dimensions entities and business definitions) to ensure consistency across dashboards and analyses.
• Translate stakeholder requirements into clear modeling deliverables (entities grains metric definitions acceptance criteria).
• Build transformations primarily in SQL leveraging Python when needed for complex logic automation or validation.
• Implement and champion data quality controls (tests reconciliations anomaly checks) tied to business-critical metrics.
• Optimize model performance in Snowflake and/or Databricks (efficient joins partitioning/clustering strategies where applicable cost/performance trade-offs) and collaborate with upstream teams on source system understanding (including NoSQL/semi-structured data) and ensure analytics models reflect correct business meaning.
• Establish modeling standards: naming conventions documentation lineage metric governance and change management for semantic definitions and support enablement: document curated datasets create user guidance and help consumers adopt the semantic layer correctly.

Required qualifications capabilities and skills

• 3 years of experience as an Analytics Engineer or related role with Masters degree in Information Technology Computer Science Management Information Systems Operations Research or related field.
• Advanced SQL skills (complex joins performance tuning incremental logic).
• Strong understanding of data modeling (facts/dimensions grains conformed dimensions SCDs metric design).
• Demonstrated experience building or operating a semantic layer / metrics framework (tool-agnostic; ability to standardize KPI logic and definitions).
• Comfort working with semi-structured data (JSON) and NoSQL sources and modeling them for analytics.
• Exposure to data governance concepts (RBAC data classification lineage audit requirements).
• Working experience with Snowflake and/or Databricks in an analytics context.
• Practical Python skills for data workflows (validation automation notebooks/scripts).
• Ability to partner with stakeholders clarify ambiguous requirements and drive to measurable outcomes.
• Strong documentation habits and attention to data correctness.

Preferred qualifications capabilities and skills

• Experience with testing and documentation.
• Familiarity with BI tooling and semantic consumption patterns (e.g. Tableau/Sigma/Looker concepts).

• Knowledge of orchestration and observability (Airflow/Dagster/ADF; logging/alerting; SLA mindset).