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Controller Transformation Analyst
mid · Data Science & Analytics
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. This is your opportunity to build cutting-edge data solutions that drive measurable improvements in efficiency, transparency, and control across the firm.
As a Reconciliation & Analysis Modernization Analyst 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.
- Perform data profiling, quality assessment, and remediation to ensure accuracy and completeness of reconciliation outputs.
- Build and automate dashboards, reporting, and monitoring solutions to provide transparency into reconciliation health and aging metrics.
- Support the migration of existing reconciliation processes from legacy tools onto the target-state Databricks platform, ensuring functional equivalence and control integrity.
- Participate in Agile ceremonies and contribute to sprint planning, backlog grooming, and iterative delivery of platform capabilities.
- 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
- Demonstrate proficiency in SQL, including complex joins, window functions, CTEs, and performance optimization across large datasets.
- Apply data engineering concepts including ETL/ELT design patterns, data modeling, data quality frameworks, and pipeline orchestration effectively.
- Reconcile discrepancies programmatically across large, complex datasets spanning multiple source systems.
- Analyze complex problems with exceptional attention to detail and a strong commitment to data accuracy.
- Communicate technical concepts clearly to non-technical stakeholders and collaborate effectively across cross-functional teams.
- Manage multiple priorities simultaneously and deliver against tight deadlines in a fast-paced environment.
- Demonstrate working knowledge of Python for data manipulation, automation, and pipeline development in a collaborative engineering setting.
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 and passion for continuous learning, emerging technologies, and driving meaningful process transformation.
- Familiarity with data visualization & low code solution tools (e.g. Tableau, Alteryx, Sigma) for building operational dashboards.