Lead Data Engineer, Applied AI Data Ingestion & Integration (DII)

09/29/2026

Address:

320 S Canal Street

Job Family Group:

Data Analytics & Reporting

Lead Data Engineer, Applied AI Data Ingestion & Integration (DII) team

Team Overview

We accelerate BMO’s AI journey by building enterprise-grade, cloud-native capabilities and AI solutions. Our team combines engineering excellence with cutting-edge AI to deliver scalable, secure, and responsible solutions that power business innovation across the bank. We are engineers, AI practitioners, platform builders, thought leaders, multipliers, and coders. Above all, we are a global team of diverse individuals who enjoy working together to create smart, secure, and scalable solutions that make an impact across the enterprise.

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 Lead Data Engineer 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.

Key Responsibilities:

Data Analysis & Integration Leadership

• 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.

Data Quality, Governance & Compliance

• 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.

AI & Advanced Analytics Enablement

• 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