JPMorganChase Hiring: Associate – Analytics Data Engineer 2026:-
JPMorganChase is hiring experienced professionals for the position of Associate – Analytics Data Engineer in Bengaluru, Karnataka, India. This is a full-time opportunity within the Commercial & Investment Bank, under the Analytics Solutions & Delivery job category.
| Company: | JPMorganChase |
| Job Role: | Associate – Analytics Data Engineer |
| Job Type: | Full-Time |
| Experience: | 3+ years with a qualifying master’s degree, or 5+ years with a qualifying bachelor’s degree |
| Salary: | As per Company Standards |
| Application Deadline: | 9 November 2026, 10:30 |
Role Overview:-
As an Associate – Analytics Data Engineer within Analytics Engineering, you will design and develop analytics-ready data models and maintain a trusted semantic layer that standardizes business metrics across dashboards and analytical applications.
You will work closely with business stakeholders and upstream engineering teams to translate requirements into reusable, governed datasets using SQL, Python, Snowflake, and Databricks.
The role focuses on dimensional data modeling, metric definitions, data quality, performance optimization, documentation, and governance. Your work will help ensure that business users can access consistent, reliable data for reporting and self-service analytics.
Educational Requirements:-
Candidates must meet one of the following education and experience combinations specified in the job description.
Option 1: Master’s Degree
- Master’s degree in Information Technology, Computer Science, Management Information Systems, Operations Research, or a related field.
- Minimum 3 years of experience in the job offered, Analytics Engineering, or a related occupation.
Option 2: Bachelor’s Degree
- Bachelor’s degree in Information Technology, Computer Science, Management Information Systems, Operations Research, or a related field.
- Minimum 5 years of experience in the job offered, Analytics Engineering, or a related occupation.
Important: The degree and experience requirements are linked. Candidates should verify the complete official job posting before applying.
Key Skills:-
1. Technical Skills
- Advanced SQL Programming
- Complex SQL Joins
- SQL Performance Tuning
- Incremental Data Processing
- Python Programming
- Data Transformation
- Data Validation and Automation
- Snowflake
- Databricks
- JSON and Semi-Structured Data Processing
- Relational and Non-Relational Data Sources
- Data Quality Testing
2. Data Engineering & Analytics Skills
- Analytics Engineering
- Dimensional Data Modeling
- Facts and Dimensions
- Grain Definition
- Conformed Dimensions
- Slowly Changing Dimensions
- Semantic Layer Development
- Metric and KPI Standardization
- Business Intelligence and Self-Service Analytics
- Data Governance
- Data Lineage and Documentation
- Data Reconciliation and Anomaly Detection
- Model Performance Optimization
- Source-to-Model Alignment
3. Tools & Technologies
- SQL
- Python
- Snowflake
- Databricks
- Tableau, Sigma, or Looker concepts
- Apache Airflow, Dagster, or Azure Data Factory (preferred)
- Logging, Alerting, and Data Observability Tools
The preferred tools listed above are examples from the job description and are not necessarily mandatory requirements.
4. Soft Skills
- Analytical and problem-solving abilities
- Stakeholder communication
- Requirements gathering and clarification
- Attention to detail
- Strong documentation habits
- Cross-functional collaboration
- Ownership and accountability
- Ability to manage ambiguous requirements
- Focus on data correctness and measurable outcomes
Roles & Responsibilities:-
Data Modeling & Analytics Engineering
- Lead the development of analytics data models optimized for reporting, business intelligence, and self-service analytics.
- Design dimensional or domain-oriented models that meet business requirements.
- Define facts, dimensions, grains, conformed entities, and metric logic.
- Build reusable datasets that support consistent reporting across teams.
- Ensure downstream analytics models accurately represent business processes.
Semantic Layer & KPI Standardization
- Design and maintain a semantic layer containing standardized metrics, dimensions, entities, and business definitions.
- Establish consistent key performance indicator (KPI) definitions across dashboards and analytical reports.
- Define semantic conventions to improve the reliability and usability of business data.
- Maintain metric logic and support consistent data interpretation across business units.
- Help stakeholders adopt reusable semantic definitions for self-service reporting.
SQL Development & Data Transformation
- Develop data transformations primarily using SQL.
- Use Python for complex logic, workflow automation, validation, and data processing when needed.
- Implement incremental data processing logic.
- Develop efficient queries and transformations for analytics workloads.
- Ensure data models meet performance and usability requirements.
Data Quality & Validation
- Implement data quality controls, including automated tests, reconciliations, and anomaly checks.
- Validate business-critical metrics and investigate discrepancies.
- Ensure the accuracy and consistency of analytical datasets.
- Establish acceptance criteria for modeling deliverables.
- Support testing and validation processes before changes are released.
Snowflake, Databricks & Performance Optimization
- Work with Snowflake and/or Databricks in an analytics engineering environment.
- Optimize data models through efficient joins and suitable partitioning or clustering strategies where applicable.
- Evaluate cost and performance trade-offs.
- Work with large and complex datasets to support scalable analytics.
- Collaborate with upstream engineering teams to ensure correct source-to-model alignment.
Data Governance & Documentation
- Establish and maintain modeling standards, naming conventions, and documentation.
- Support data lineage, metric governance, and change management.
- Apply relevant data governance concepts, including role-based access control, data classification, and audit requirements.
- Document curated datasets and semantic definitions.
- Maintain clear documentation to improve trust, traceability, and data reuse.
Stakeholder Collaboration & Analytics Enablement
- Partner with stakeholders to understand business needs and clarify ambiguous requirements.
- Translate business requirements into modeling deliverables, metric definitions, and acceptance criteria.
- Coordinate with upstream engineering teams to understand source systems, including semi-structured and non-relational data sources.
- Create user guidance and help analytics consumers use datasets and semantic layers correctly.
- Drive measurable improvements in reporting consistency and analytics usability.
About the Company:-
JPMorganChase is a global financial services institution with a history spanning more than 200 years. It provides financial solutions to consumers, small businesses, corporations, institutional clients, and government organizations.
Its businesses include investment banking, consumer and small business banking, commercial banking, financial transaction processing, and asset management through the J.P. Morgan and Chase brands.
The company operates across global financial markets and uses technology, data, and analytics to support business operations and decision-making.
JPMorganChase Hiring: Associate – Analytics Data Engineer 2026 Application Process:-
Apply Link:- Click Here To Apply
Note:- Only the shortlisted candidates will be notified for the further interview process.
