J.P. Morgan Chase, a 200‑year‑old financial powerhouse, operates across investment banking, commercial banking, consumer finance, and asset management. With a presence in more than 100 countries, the firm is renowned for its commitment to innovation, risk management, and client‑centric solutions. In India, JPMorgan has built a vibrant technology ecosystem that fuels its global operations, offering employees exposure to cutting‑edge platforms, large‑scale data environments, and a culture that prizes collaboration and continuous learning. The Commercial & Investment Bank (CIB) division, where this role sits, serves corporations, governments, and institutions, delivering strategic advice, capital markets solutions, and risk‑mitigation services. The Associate – Analytics Data Engineer will be a key member of the Analytics Engineering team, responsible for creating analytics‑ready data models and a trusted semantic layer that standardises business metrics across the organisation. Working closely with business stakeholders, data owners, and upstream engineering teams, you will translate complex requirements into well‑designed, governed datasets in Databricks and Snowflake, primarily using SQL and Python. Your work will enable self‑service reporting, improve data reliability, and accelerate decision‑making across the CIB business. Key Responsibilities: 1. Lead the design and development of dimensional and domain‑oriented data models optimised for BI and self‑service consumption. 2. Build and maintain a semantic layer that defines standard metrics, dimensions, and business definitions. 3. Convert stakeholder requirements into clear modelling deliverables, including entity definitions, grain specifications, and acceptance criteria. 4. Write performant SQL transformations and leverage Python for complex logic, automation, and validation tasks. 5. Implement data‑quality controls such as tests, reconciliations, and anomaly detection tied to critical business metrics. 6. Optimise model performance in Snowflake and/or Databricks through efficient joins, clustering, and cost‑performance trade‑offs. 7. Collaborate with upstream teams to ensure source‑to‑model alignment, especially for semi‑structured and non‑relational data sources. 8. Establish and enforce modelling standards, naming conventions, documentation, lineage, and metric governance. 9. Produce comprehensive documentation and user guides to drive adoption of the semantic layer. 10. Mentor junior team members and contribute to continuous improvement of analytics engineering practices. Tech Stack: SQL, Python, Snowflake, Databricks, JSON, data‑governance tools, BI platforms (Tableau, Sigma, Looker), orchestration tools (Airflow, Dagster, Azure Data Factory). Growth Path: Starting as an Associate, high performers can progress to Senior Analytics Engineer, Analytics Solutions Lead, or move into Data Architecture and Product Management roles within the broader CIB technology ecosystem. Why Join JPMorgan? The firm offers a global platform, exposure to massive data volumes, and the chance to work on mission‑critical financial products. Employees benefit from robust learning programmes, mentorship, and a culture that values diversity, inclusion, and work‑life balance. The role provides a unique blend of technical depth and business impact, making it an ideal springboard for a long‑term career in data engineering and analytics.