Associate Analytics Engineer

Role Overview: You are a strategic thinker passionate about driving solutions in Analytics. As an Associate Analytics Engineer within the Data & Analytics team at JPMorgan Chase, you will play a crucial role in building analytics-ready data models and a trusted semantic layer that standardizes business metrics. Your primary responsibility will be translating stakeholder needs into governed datasets and KPI definitions using SQL as the primary transformation language. You will also focus on embedding data quality, documentation, and performance best practices for downstream teams to reliably reuse models for self-service reporting. Key Responsibilities: - Lead the development of dimensional and/or domain-oriented analytics data models optimized for BI and self-service consumption. - Design and maintain a semantic layer defining standardized metrics, dimensions, entities, and business definitions. - Translate stakeholder requirements into clear modeling deliverables, including grains, entities, metric logic, and acceptance criteria. - Build transformations primarily in SQL and leverage Python for complex logic, automation, and validation as needed. - Implement data quality controls, including tests, reconciliations, and anomaly checks tied to business-critical metrics. - Optimize model performance in Snowflake and/or Databricks by applying efficient joins and storage/performance strategies. - Collaborate with upstream teams to align source-to-model meaning, including semi-structured and NoSQL data considerations. - Establish modeling standards covering naming conventions, documentation, lineage, metric governance, and change management. - Document curated datasets and produce user guidance to enable correct adoption of semantic definitions. - Support consumers by troubleshooting metric questions and improving usability of downstream analytics products. Qualifications Required: - Hold a Masters degree in IT, Computer Science, MIS, Operations Research, or related field plus 3 years of relevant experience, or hold a Bachelors degree in the same fields plus 5 years of relevant experience. - Demonstrate advanced SQL capability, including complex joins, performance tuning, and incremental logic. - Apply strong data modeling expertise across grains, facts/dimensions, conformed dimensions, SCDs, and metric design. - Build or operate a semantic layer or metrics framework to standardize KPI logic and definitions. - Model semi-structured data (e.g., JSON) and integrate NoSQL sources for analytics use cases. - Use Snowflake and/or Databricks effectively in an analytics engineering context and apply practical Python for workflow automation and validation. - Practice strong stakeholder partnership and documentation discipline to drive clarity, correctness, and measurable outcomes. (Note: Additional details of the company were provided in the job description but have been omitted as per the instructions.) Role Overview: You are a strategic thinker passionate about driving solutions in Analytics. As an Associate Analytics Engineer within the Data & Analytics team at JPMorgan Chase, you will play a crucial role in building analytics-ready data models and a trusted semantic layer that standardizes business metrics. Your primary responsibility will be translating stakeholder needs into governed datasets and KPI definitions using SQL as the primary transformation language. You will also focus on embedding data quality, documentation, and performance best practices for downstream teams to reliably reuse models for self-service reporting. Key Responsibilities: - Lead the development of dimensional and/or domain-oriented analytics data models optimized for BI and self-service consumption. - Design and maintain a semantic layer defining standardized metrics, dimensions, entities, and business definitions. - Translate stakeholder requirements into clear modeling deliverables, including grains, entities, metric logic, and acceptance criteria. - Build transformations primarily in SQL and leverage Python for complex logic, automation, and validation as needed. - Implement data quality controls, including tests, reconciliations, and anomaly checks tied to business-critical metrics. - Optimize model performance in Snowflake and/or Databricks by applying efficient joins and storage/performance strategies. - Collaborate with upstream teams to align source-to-model meaning, including semi-structured and NoSQL data considerations. - Establish modeling standards covering naming conventions, documentation, lineage, metric governance, and change management. - Document curated datasets and produce user guidance to enable correct adoption of semantic definitions. - Support consumers by troubleshooting metric questions and improving usability of downstream analytics products. Qualifications Required: - Hold a Masters degree in IT, Computer Science, MIS, Operations Research, or related field plus 3 years of relevant experience, or hold a Bac