JPMorganChase's 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. You’ll 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 what’s 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 Master's 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).