JPMorgan Chase—Lead Software Engineer - Cognos, BO, Snowflake,SQL

Job description

Description: We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank's Sales & Research Technology team, you will develop semantic layers, data models, and dashboards, and partner with business and data teams to deliver reliable insights at scale. you will play an important role in our BI modernization strategy, including the SAP BusinessObjects exit program and establishing a scalable foundation for long-term platform consolidation and self-service analytics and support the long-term BI strategy — transitioning toward Sigma Computing, Snowflake/Databricks, and the Master Data Lake (MDL). Job responsibilities - Build and enhance end-to-end BI solutions: requirements → data modeling → report/dashboard development → testing → production support. - Deliver the SAP BO exit outcomes by migrating and modernizing reporting content into IBM Cognos, including report rebuilds, validation, scheduling, and cutover support. - Develop and maintain IBM Cognos reports, packages, and frameworks (Framework Manager / data modules as applicable). - Design performant datasets and reporting layers on Snowflake and/or Databricks, including SQL optimization and cost/performance tuning. - Partner with data owners and data engineering to align reporting to governed, reusable datasets and data products (MDL aligned), reducing point-to-point extracts and improving consistency. - Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team. - Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation. Follow SDLC practices (version control, peer reviews, release management) and contribute to standards for BI development. - Implement data quality checks, reconciliation, and monitoring to ensure trusted reporting during migrations and ongoing operations. - Support incident triage and root-cause analysis for reporting/data issues; drive permanent fixes and documentation. - Contribute to long-term BI consolidation by building reusable metric definitions, semantic objects, and migration playbooks that support future dashboard and self-service tooling adoption (including Sigma where applicable). - Participate in Sigma Computing PoC — build dashboards, connect to Snowflake/Databricks via MDL. Collaborate with data owners to register data sources to MDL. Assess and migrate Tableau/QlikSense dashboards to Sigma. Evaluate Sigma maturity for future Cognos workload migration. Support AI/conversational analytics enablement Required qualifications, capabilities, and skills - Formal training or certification on software engineering concepts and 5+ years applied experience - Strong hands-on experience with SAP Business Objects and IBM Cognos (report authoring, packages/models, scheduling, performance troubleshooting). - Hands-on experience with Snowflake and/or Databricks (SQL, data modeling, working with large datasets). - Strong SQL skills (joins, window functions, aggregations, query tuning). - Experience delivering production-grade reporting/analytics solutions with attention to reliability, supportability, and governance. - Ability to translate business requirements into scalable reporting and data designs, including migration and coexistence scenarios. - Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security. - Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices - Hands-on experience with one of the tool QlikSense or Tableau or Sigma — dashboarding, data visualization best practices, migration experience - BI (reports/dashboards) end to end migration project experience (any platform). Experience analyzing, rationalizing, and migrating large report inventories, including prioritization, wave planning, and execution tracking. - Working knowledge of data warehousing concepts (for example: dimensional modeling, facts and dimensions, and data marts) sufficient to assess migration impacts and dependencies. Preferred qualifications, capabilities, and skills - Sigma (workbook/dashboard development, warehouse-native analytics concepts) - Cloud data platforms — AWS, data lake architectures, MDL / data mesh / data product concepts - Experience using analytics automation or artificial intelligence-enabled tools (e.g. Databricks Genie, Snowflake Cortex, Chat GPT/Copilot) to accelerate dataset analysis or workflow efficiency in a controlled enterprise environment. - Python (data processing, automation, testing, utilities). - Experience creating migration factory tooling, templates, or automated validation for large report portfolios. - Financial services / Capital Markets / CIB domain experience