Senior Data Analyst

First Chicago is hiring a Senior Data Analyst as the dedicated data resource for its Strategic Finance unit. You will build and maintain the data extracts, analytical datasets, and reproducible reports the unit relies on, drawn directly from the enterprise data lake and source systems, so its analysis rests on timely, reconciled, and documented data.

You will report to the Chief Accounting Officer & Treasurer. Your first priority is supporting the Senior Strategy & Performance Analyst's investigations, action tracking, and executive reporting, with Claims data first. Financial Planning & Analysis and special projects are supported as capacity permits.

The role supplies data and reporting to the unit. It does not own enterprise KPI definitions, source systems, or the conclusions drawn from the analysis.

• →Build and maintain data extracts and analytical datasets from the enterprise data lake and source systems, including claims, policy, premium, and financial data, at the level investigations require: carrier, state, program, product, distribution channel, department, and vendor.
• →Develop reproducible, documented queries and reports that can be refreshed, reviewed, and rerun without manual rework.
• →Reconcile datasets to the general ledger, financial reporting, and system control totals before release; document reconciling differences and their causes.
• →Identify and document data-quality issues; escalate source-system defects to the responsible data owners and track them to resolution.
• →Maintain a data dictionary and lineage documentation for the unit's datasets, applying enterprise KPI definitions where they exist and documenting any supplemental definitions.
• →Supply the data behind action tracking and monthly executive summaries, including documented baselines and post-implementation measurement of improvement initiatives.
• →Use existing enterprise reports where they are timely and sufficient; build supplemental data and reports only where they are not.
• →Automate recurring extracts and reports where automation reduces manual effort and error risk.
• →As capacity permits, support Financial Planning & Analysis with data for budgeting, forecasting, and variance analysis, and special projects with data and analysis as assigned.

• Strategy & performance analysis (primary): Claims data first: severity and frequency, loss and loss adjustment expense, loss development, cycle times, attorney representation, litigation, complaints, vendor costs, and state/program variances. Data for other business areas as assignments require.
• Financial Planning & Analysis (as capacity permits): Data extracts and supporting datasets for budgeting, forecasting, modeling, and variance analysis.
• Special projects (as capacity permits): Data and analysis support as assigned.

The analyst supplies data; actuarial analyses and judgments remain with Actuarial, and source systems and data corrections remain with their owners.

• Bachelor's degree in Data Analytics, Statistics, Computer Science, Information Systems, Finance, Accounting, Economics, or a related field, or equivalent experience.
• Five or more years of data analysis, business intelligence, or reporting experience, including work in insurance or financial services. P&C insurance data experience strongly preferred.
• Advanced SQL, including complex joins, window functions, and query performance on large datasets; experience in a data lake or cloud data warehouse environment.
• Proficiency with BI/data visualization tools and advanced Excel. Scripting experience (such as Python) for data preparation and automation preferred.
• Working knowledge of P&C insurance data: policy and premium, claims, loss and loss adjustment expense transactions, accident-year and calendar-year views, and financial reporting concepts.
• Disciplined approach to validation, reconciliation, documentation, and version control; ability to explain data limitations clearly to non-technical audiences; discretion with confidential information.

Timely delivery of data for investigations; datasets that reconcile to control totals at release; documented, reproducible queries and reports that refresh without rework; reduced manual effort through automation; data-quality issues identified, escalated, and tracked to resolution; and confidence among users in the numbers provided. Success is not measured by the volume of reports or dashboards produced.