Senior Manager, Data Engineering, Applied AI Data Ingestion & Integration (DII)

BMO • Chicago, IL

10d • $120,000 - $250,000 • Hybrid

About The Position

The Senior Manager, Data Engineering on BMO’s Applied AI Data Ingestion & Integration (DII) team helps enable enterprise AI and advanced analytics capabilities. The role leads the analysis, profiling, integration, quality assessment, and operationalization of structured, semi-structured, unstructured, and multimodal data used across AI, machine learning, and Generative AI applications. It is an individual-contributor role at the Senior Manager level and is hybrid.

The position works closely with technology teams, cross-POD leads, data engineers, data scientists, architects, AI engineers, and business stakeholders to advance enterprise adoption of Generative AI and agentic flows. It supports AI-ready datasets, multimodal document ingestion pipelines, RAG solutions, and connectors, resources, and tools for Model Context Protocol (MCP). The successful candidate owns technical outcomes, mentors engineers, manages ambiguity, and drives measurable improvements in platform capability, delivery quality, operational resilience, and business value.

BMO’s DII team builds end-to-end services to move, prepare, and operationalize data for AI and analytics workloads. The broader team develops cloud-native capabilities and AI solutions intended to be scalable, secure, and responsible. BMO describes its purpose as “Boldly Grow the Good in business and life” and emphasizes inclusion, integrity, and responsible innovation.

Requirements

• 8–10 years of experience in Data Engineering/support or related analytics disciplines; enterprise experience is preferred.
• At least 3 years of experience as a Technical Delivery leader, driving and owning the work.
• Demonstrated experience leading technical delivery for enterprise data integration, data warehousing, ETL/ELT processes, cloud data platforms, data governance, and production analytics or AI platforms.
• Strong proficiency in SQL, Python, or similar technologies, and experience with large-scale datasets across cloud and on-premises environments.
• Experience with analytical and visualization tools such as Power BI, Tableau, Python, Azure Data Factory, Azure AI Search, or equivalent technologies.
• Strong communication and stakeholder-management skills, including the ability to influence decisions across business and technology organizations.
• Ability to lead initiatives, manage priorities, and deliver results in fast-paced, highly regulated environments.
• Familiarity with MLOps, CI/CD, and cloud-based AI infrastructure.
• Knowledge of Agile delivery methodologies and experience working in cross-functional product teams.
• Commitment to BMO’s values of inclusion, integrity, and responsible innovation.
• Bachelor’s degree in computer science, information systems, data analytics, statistics, engineering, mathematics, business analytics, or a related quantitative field.

Nice To Haves

• Experience in financial services, banking, risk, compliance, or another highly regulated industry.
• Knowledge of Responsible AI, Model Risk Management, SR 11-7, OSFI E-23, or related governance frameworks.
• Master’s degree in data science, analytics, computer science, information management, business administration, or a related field.
• Relevant certifications in data management, cloud platforms (Azure/AWS), analytics, AI, or data governance.

Responsibilities

• Lead enterprise data onboarding, ingestion, and integration initiatives supporting AI, analytics, and business intelligence use cases.
• Partner with Product Owners, Data Engineers, Data Scientists, and business stakeholders to translate business needs into data requirements, data models, and integration strategies.
• Design and implement reliable, scalable ingestion and integration pipelines for structured, semi-structured, unstructured, and multimodal data, ensuring it is AI-ready, governed, secure, and observable.
• Ensure pipelines meet enterprise governance, access-control, and security standards, including role-based access and lineage considerations; monitor performance, troubleshoot failures, and optimize cost and throughput.
• Document processes, share knowledge, and contribute to continuous learning and responsible innovation.
• Establish and monitor data-quality standards, controls, and metrics for accuracy, completeness, timeliness, and consistency.
• Partner with Data Governance, Risk, Compliance, and Model Risk Management teams to support adherence to enterprise data policies, regulatory requirements, and Responsible AI standards.
• Support data lineage, metadata management, cataloging, and traceability across ingestion and integration platforms.
• Collaborate with AI and Data Science teams to prepare, validate, and optimize datasets for machine learning, Generative AI, and advanced analytics.
• Support multimodal data ingestion initiatives involving documents, images, audio, video, and enterprise knowledge repositories.
• Analyze chunking, indexing, retrieval, and data-preparation strategies used in RAG and AI Search solutions.
• Develop production-grade services and AI capabilities using Python, REST APIs, JSON/XML, vector databases, and RAG evaluation and retrieval metrics.
• Develop analytical frameworks and KPIs to measure platform effectiveness, ingestion performance, data-quality trends, and business outcomes.
• Evaluate emerging AI and data-management technologies and recommend opportunities to improve DII capabilities.
• Lead cross-functional initiatives from discovery through production in partnership with business, technology, architecture, risk, security, and operations teams.
• Mentor and guide analysts and junior team members, fostering continuous learning, analytical excellence, and technical accountability.
• Communicate technical findings, risks, and recommendations to technical and non-technical audiences, including senior leadership.
• Drive continuous-improvement initiatives that increase automation, operational efficiency, and scalability of data ingestion and integration processes.

Benefits

• Health insurance
• Tuition reimbursement
• Accident insurance
• Life insurance
• Retirement savings plans
• The total compensation package may include performance-based incentives, discretionary bonuses, and other perks and rewards.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior