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About the Role

This is an Individual Contributor role for a professional who can run initiatives and drive the results. The Applied AI Data Ingestion & Integration (DII) Team provides end‑to‑end services to help move, prepare, and operationalize data for AI and analytics workloads. As a Senior Manager within the (DII) Team, you will play a key role in enabling BMO's AI and advanced analytics capabilities by transforming complex business requirements into scalable data solutions. You will lead the analysis, profiling, integration, quality assessment, and operationalization of structured, semi-structured, and unstructured data used across AI, machine learning, and Generative AI applications. This position involves close collaboration with various technology teams, Cross POD leads, Data Engineers, Data Scientists, Architects, AI and Data engineers, and Business stakeholders to push the adoption of Generative AI technologies and agentic flows across enterprise-wide applications and processes, and to develop partnerships with third-party providers for validating and adopting production-grade solutions. Your work will directly support the development of AI-ready datasets, multimodal document ingestion pipelines, Retrieval-Augmented Generation (RAG) solutions, Building various connectors, resources, and tools for (Model Context Protocol) MCPs. This role requires deep technical expertise, strong engineering judgement, and the ability to lead through influence. The successful candidate will be expected to own technical outcomes, mentor engineers, manage ambiguity, and drive measurable improvements in platform capability, delivery quality, operational resilience, and business value.

Responsibilities

Provide deep hands-on technical leadership with enterprise data onboarding, ingestion, and integration initiatives supporting AI, analytics, and business intelligence use cases.
Lead and partner with Product Owners, Data Engineers, Data Scientists, and business stakeholders to translate business needs into actionable data requirements, data models, and integration strategies.
Design and implement reliable, scalable data ingestion and integration pipelines for structured, semi-structured, unstructured data (e.g., databases, files, documents, APIs, events), and multi-modal data, ensuring data is AI ready, governed, secure, and observable.
Ensure pipelines follow enterprise governance, access control, and security standards, including role-based access and lineage considerations.
Monitor pipeline performance, troubleshoot failures, and optimize cost and throughput.
Document processes, share knowledge, and contribute to a culture of continuous learning and responsible innovation.
Establish and monitor data quality standards, controls, and metrics to ensure accuracy, completeness, timeliness, and consistency.
Partner with Data Governance, Risk, Compliance, and Model Risk Management teams to ensure adherence to enterprise data policies, regulatory requirements, and Responsible AI standards.
Support data lineage, metadata management, data cataloging, and traceability capabilities 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 applications.
Support multimodal data ingestion initiatives involving documents, images, audio, video, and enterprise knowledge repositories.
Analyze performance and effectiveness of 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, RAG evaluation and retrieval metrics.
Develop analytical frameworks and KPIs to measure data platform effectiveness, ingestion performance, 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, partnering with business, technology, architecture, risk, security, and operations teams.
Provide mentorship and guidance to analysts and junior team members, fostering a culture of continuous learning and analytical excellence and technical accountability.
Communicate complex technical findings, risks, and recommendations to both technical and non-technical audiences, including senior leadership.
Own and drive continuous initiatives that increase automation, operational efficiency, and scalability of data ingestion and integration processes.

Requirements

8-10 years of experience in Data Engineering/ support, or related analytics disciplines, preferably within large enterprise environments.
3+ years experience as a Technical Delivery Leader (who owned the work).
Demonstrated experience in 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, and experience working with large-scale datasets across cloud and on-premises environments.
Experience using 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, with the ability to influence decisions across business and technology organizations.
Demonstrated 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 within cross-functional product teams.
Experience working within financial services, banking, risk, compliance, or other highly regulated industries is a plus.
Knowledge of Responsible AI, Model Risk Management, SR 11-7, OSFI E-23, or related governance frameworks is a plus.
Commitment to BMO’s values of inclusion, integrity, and responsible innovation.

Skills

SQL
Python
Power BI
Tableau
Azure Data Factory
Azure AI Search
MLOps
CI/CD
REST APIs
JSON/XML
Vector databases
RAG evaluation
Retrieval metrics

Location

100 King Street West

Work Type

Hybrid

Experience Level

Senior
Individual Contributor

Education Level

Bachelor’s degree in computer science, Information Systems, Data Analytics, Statistics, Engineering, Mathematics, Business Analytics, or a related quantitative field.
Master’s degree in data science, Analytics, Computer Science, Information Management, Business Administration, or a related field is preferred.
Relevant certifications in Data Management, Cloud Platforms (Azure/AWS), Analytics, AI, or Data Governance are considered an asset.

Salary/Compensations

$105,000.00 - $215,000.00

Benefits

Health insurance
Tuition reimbursement
Accident and life insurance
Retirement savings plans

About the Company

At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting, positive change for our customers, our communities and our people. By working together, innovating and pushing boundaries, we transform lives and businesses, and power economic growth around the world.
As a member of the BMO team you are valued, respected and heard, and you have more ways to grow and make an impact. We strive to help you make an impact from day one – for yourself and our customers. We’ll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in-depth training and coaching, to manager support and network-building opportunities, we’ll help you gain valuable experience, and broaden your skillset.
To find out more visit us at https://jobs.bmo.com/ca/en.

Equal Opportunity

BMO is committed to an inclusive, equitable and accessible workplace. By learning from each other’s differences, we gain strength through our people and our perspectives. Accommodations are available on request for candidates taking part in all aspects of the selection process. To request accommodation, please contact your recruiter.