Senior Data Engineer - Reporting Automation & AI

BMO•Chicago, IL

2d•$75,000 - $160,000•Hybrid

About The Position

The Senior Data Engineer - Reporting Automation & AI designs, develops, and maintains automated reporting solutions that improve the efficiency, accuracy, and scalability of risk-reporting processes. This role combines reporting automation, AI-enabled workflows, application development, and technical problem solving to modernize legacy processes and support future-state reporting platforms. The successful candidate will work closely with risk-reporting teams to translate manual, fragmented processes into reliable automated pipelines. This person will serve as the technical bridge between raw risk data and the accurate, timely reports stakeholders rely on. This is a net-new hybrid role based in Downtown Chicago. The successful candidate will work from the Chicago office 3 days per week, with Tuesday as a required in-office day.

Requirements

4+ years of hands-on professional work experience in reporting automation or data development, including advanced SQL with complex views, stored procedures, and performance tuning.
2+ years of experience using both Python and Shell scripting to automate reporting workflows and build supporting applications.
2+ years of experience integrating AI functionality into existing processes through APIs or comparable AI-enabled workflow tooling.
1 year of experience with Power BI and Power Query.
1 year of experience using Git for version control.
3+years of experience with dimensional modeling and modern data-warehouse concepts.
Experience modernizing legacy reporting or data processes; SAS-to-Python migration experience is strongly preferred.
Ability to work independently, take initiative, and manage technical work with limited day-to-day direction.
Strong collaboration and communication skills, including the ability to work effectively with data-reporting teams and explain technical ideas concisely.
Professional Experience: A few years of hands-on experience in reporting automation or data development, with a strong mastery of SQL (complex views, stored procedures, and performance tuning).
Automation & Development: Proven ability to use Python and Shell scripting to automate reporting workflows and build supporting applications.
AI Enablement: Familiarity applying AI-enabled workflows and tooling to modernize and accelerate reporting processes.
Engineering Practices: Version control using Git and a solid grasp of dimensional modeling and modern data warehouse concepts.
Independence: A self-starter with the technical maturity to manage deliverables and troubleshoot complex problems with minimal supervision.

Nice To Haves

Power BI (DAX / Power Query) is a plus
familiarity with Microsoft Fabric is also a plus

Responsibilities

Design, develop, and maintain automated reporting solutions that improve the efficiency, accuracy, and scalability of risk-reporting processes.
Build and integrate AI-enabled workflows that streamline data preparation, validation, and report generation.
Develop and maintain Python and Shell applications and scripts that support reporting automation, monitor system health, and improve operational reliability.
Modernize manual and legacy reporting processes into scalable automated pipelines, including migration from SAS-based processes to Anaconda and Python-based solutions.
Design optimized data marts and star schemas for Power BI consumption.
Develop and tune SQL queries to deliver accurate, high-performing risk-reporting outputs.
Partner with data-reporting teams to ensure data structures and outputs support effective reporting and visualization.
Communicate technical ideas, tradeoffs, and solutions clearly to technical stakeholders.
Reporting Automation: Design, develop, and maintain automated reporting solutions that improve the efficiency, accuracy, and scalability of risk reporting processes.
AI-Enabled Workflows: Build and integrate AI-enabled workflows to streamline data preparation, validation, and report generation, reducing manual effort and turnaround time.
Application Development: Develop and maintain applications and scripts (Python / Shell) that support reporting automation, monitor system health, and improve the reliability of risk reporting operations.
Legacy Modernization: Re-engineer legacy, manual reporting processes into modern, automated pipelines that support future-state reporting platforms.
Reporting Delivery: Design optimized data marts and star schemas tailored for Power BI consumption, and optimize SQL to deliver accurate risk reporting outputs.
Technical Problem Solving: Independently investigate data quality and reporting issues, identify root causes, and implement durable technical remediations.
Clear Communication: Communicate clearly with both technical and non-technical stakeholders to ensure alignment, transparency, and data integrity across risk reporting.