Are you passionate about modernizing critical financial processes through technology and automation? Join the Reconciliation & Analysis Modernization team within the Controller Transformation Office, where you will help redesign and re-engineer how JPMorganChase reconciles, validates, and analyzes financial data at scale. You will have the opportunity to work with cutting-edge cloud-native technologies, influence the future-state architecture of key controllership functions, and contribute to a culture of engineering excellence and continuous improvement. At JPMorganChase, your technical expertise will directly support the integrity of the firm's financial reporting and controls environment.
As a Reconciliation & Analysis Modernization Associate within the Controller Transformation Office, you will design, develop, and partner with engineering to deploy data pipelines, automated reconciliation logic, and analytical solutions using Databricks, Python, and SQL. You will partner closely with Controllers, Technology, and Operations stakeholders to migrate legacy reconciliation processes onto modern, scalable platforms. Your role will involve translating complex business requirements into robust technical solutions, ensuring data quality and accuracy, and driving measurable improvements in efficiency, transparency, and control. You will thrive in an environment that values technical rigor, intellectual curiosity, and a relentless focus on operational excellence.
Job Responsibilities
- Design, build, and maintain automated reconciliation and analysis workflows using Databricks, Python, and SQL to replace manual and legacy processes.
- Develop scalable data pipelines and transformation logic within the Databricks Lakehouse environment to support high-volume financial data processing.
- Write clean, well-tested Python code to automate break identification, root cause analysis, and exception reporting across reconciliation processes.
- Author and optimize complex SQL queries for data extraction, validation, and reconciliation across multiple source systems and ledgers.
- Collaborate with Controllers, Technology partners, and business stakeholders to gather requirements and translate them into technical design specifications.
- Build and automate dashboards, reporting, and monitoring solutions to provide transparency into reconciliation health and aging metrics.
- Document technical designs, data lineage, and process flows to support auditability and knowledge transfer.
- Drive continuous improvement by identifying opportunities to enhance automation coverage, reduce manual touchpoints, and improve cycle times.
Required Qualifications, Capabilities, and Skills
- 2+ years of hands-on experience writing Python for data engineering, automation, or analytical applications.
- Strong proficiency in SQL, including complex joins, window functions, CTEs, and performance optimization across large datasets.
- Experience working within Databricks or Apache Spark environments for data transformation and pipeline development.
- Solid understanding of data engineering concepts including ETL/ELT design patterns, data modeling, data quality frameworks, and pipeline orchestration.
- Demonstrated ability to work with large, complex datasets across multiple source systems and reconcile discrepancies programmatically.
- Strong analytical and problem-solving skills with exceptional attention to detail and a commitment to data accuracy.
- Ability to communicate technical concepts clearly to non-technical stakeholders and collaborate effectively across cross-functional teams.
- Strong organizational skills with the ability to manage multiple priorities and deliver against tight deadlines in a fast-paced environment.
- Bachelor's degree in Computer Science, Engineering, Data Science, Finance, or a related quantitative discipline.
Preferred Qualifications, Capabilities, and Skills
- Experience in financial services, particularly within Controllership, Finance, or Operations functions involving reconciliation, subledger-to-general ledger analysis, or financial controls.
- Familiarity with banking products and accounting concepts such as Loans, Deposits, Derivatives, Securities, or Cash.
- Experience with Agile delivery methodologies and tools including JIRA and Confluence.
- Exposure to cloud platforms such as AWS in conjunction with Databricks.
- Experience with software development best practices in a collaborative engineering environment.
- Knowledge of data governance, lineage, and metadata management practices.
- Familiarity with data visualization & low code solution tools (e.g. Tableau, Alteryx, Sigma) for building operational dashboards.