Analytics Solutions Manager- Associate

JPMorgan Chase Bank; Bengaluru; On-site; INR 1,500,000 - 2,400,000; Full time

Job summary

JPMorgan Chase Bank in Bengaluru seeks an Analytics Solutions Manager- Associate to translate stakeholder needs into governed data models and KPIs using SQL in Snowflake and Databricks. You will embed data quality, documentation, and performance practices for reusable self-service analytics.

Responsibilities include designing dimensional data models, maintaining a semantic layer, and guiding data quality with tests and validations.

Qualifications

• Hold a Master+s degree in IT, Computer Science, MIS, Operations Research, or related field plus 3 years of relevant experience, or hold a Bachelor+s 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.

Responsibilities

• Lead 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.

Skills

Advanced SQL

Data modeling

Stakeholder collaboration

Documentation discipline

Python scripting

Education

Master's degree in IT / CS / MIS / OR

Bachelor's degree in IT / CS / MIS / OR

Tools

Snowflake

Databricks

NoSQL

Job description

If you are a strategic thinker passionate about driving solutions in analytics, you have found the right team.

As an Analytics Solutions Manager- Associate within our analytics organization, you lead development of dimensional and domain-oriented analytics data models optimized for BI and self-service consumption. You will translate stakeholder needs into governed datasets and KPI definitions in Snowflake and/or Databricks using SQL as the primary transformation language. You will embed data quality, documentation, and performance best practices so downstream teams can reliably reuse models for self-service reporting.

Job Responsibilities

• Lead 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.

Required Qualifications, Capabilities, and Skills

• Hold a Master s degree in IT, Computer Science, MIS, Operations Research, or related field plus 3 years of relevant experience, or hold a Bachelor s 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.

Preferred Qualifications, Capabilities, and Skills

• Leverage experience with testing and documentation frameworks for analytics engineering.

• Apply familiarity with BI consumption patterns and tooling concepts (e.g., Tableau, Sigma, Looker).

• Utilize orchestration tooling knowledge (e.g., Airflow, Dagster, ADF) with an SLA and reliability mindset.

• Implement observability practices such as logging, alerting, and operational runbooks for data products.

• Optimize cost and performance trade-offs through pragmatic platform tuning and design decisions.