Data Analyst

The interview process at Consumer & Community Banking typically begins with a timed online aptitude test, followed by a technical interview focusing on SQL query formulation, Python data‑wrangling, and basic finance concepts. The final HR round assesses cultural fit, communication skills and motivation. Candidates should practice data‑reconciliation scenarios and be ready to discuss any finance‑related projects.

Consumer & Community Banking promotes a collaborative environment where analysts work closely with finance and technology teams, encouraging continuous learning and cross‑functional exposure. The division emphasizes work‑life balance, flexible work arrangements and clear career progression pathways, backed by robust mentorship programs.

Bachelor's degree in Computer Science, Information Technology, Electronics, Mathematics, Statistics or related field; Minimum 60% aggregate (or equivalent CGPA); No active backlogs; Fresh graduates from 2025 or 2026 batches are preferred; Strong analytical mindset and willingness to learn financial concepts.

Round 1: Online assessment (aptitude & logical reasoning) → Round 2: Technical interview (SQL/Python & finance domain) → Round 3: HR interview (fit, motivation and cultural alignment)

JPMorgan Chase, a 200‑year‑old financial powerhouse, operates across investment banking, consumer banking, commercial banking, payments and asset management. Its Consumer & Community Banking (CCB) division serves millions of retail customers in the United States with credit cards, mortgages, auto loans, personal banking and digital solutions. The division is known for leading credit‑card sales, strong deposit growth and high customer‑satisfaction scores, thanks to a culture that blends deep financial expertise with cutting‑edge technology. In India, JPMorgan Chase is expanding its analytics capabilities to support global operations, offering fresh talent exposure to large‑scale data ecosystems and cross‑border financial products. The role of Data Analyst in the CCB Data & Analytics team is a foundational position that bridges finance and technology. You will work closely with finance partners, branch operations teams and data engineers to ingest, cleanse and transform branch performance and occupancy‑cost data from legacy systems into the modern Databricks lakehouse. Your analyses will directly influence cost‑allocation decisions, branch profitability assessments and strategic planning for the bank’s extensive branch network. The position demands strong SQL and Python skills, an eye for data quality, and the ability to communicate insights clearly to non‑technical stakeholders. Key responsibilities include: 1. Gather, extract and stage branch network and occupancy‑cost data from legacy sources and Databricks for finance reporting. 2. Develop and maintain robust SQL queries and Python‑based ETL workflows for recurring reports and ad‑hoc analysis. 3. Document data definitions, business rules and source‑to‑target mappings to support the legacy‑to‑Databricks migration. 4. Perform reconciliations between legacy outputs and new datasets, investigating variances and tracking resolutions. 5. Translate finance partners’ questions into precise analytical requirements and deliver explainable results. 6. Contribute to ThoughtSpot dashboards and other visualisation artefacts, ensuring timely delivery of insights. 7. Implement data‑governance controls, validation checks and change‑traceability mechanisms. 8. Identify and propose process‑automation opportunities to reduce manual reporting effort. 9. Collaborate with data‑engineering and technology teams to enhance data pipelines and ensure scalability. 10. Stay updated on financial cost concepts such as P&L statements, occupancy‑cost components and allocation hierarchies. Tech stack: SQL (Teradata/Redshift), Python (pandas, numpy), Databricks, ThoughtSpot, Microsoft Excel, Git for version control, and exposure to Intelligent Automation tools (e.g., UiPath, Blue Prism). Growth path: Starting as a Data Analyst, you can progress to Senior Analyst, Analytics Lead, and eventually to Manager – Analytics or Finance Business Partner roles, with opportunities to move into data‑science or product‑analytics tracks across the global JPMorgan Chase ecosystem. Why join? You will gain hands‑on experience with enterprise‑grade data platforms, work on high‑impact financial analytics that shape branch strategy, and be part of a globally recognised brand that invests heavily in employee development, mentorship and diversity. The role offers a blend of finance domain exposure and technical depth, making it an ideal launchpad for a career in data‑driven financial services.