Lead Data Engineer, Applied AI Data Ingestion & Integration (DII)
BMO
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Benefits offered by this job
Job summary
BMO is seeking a Lead Data Engineer in Chicago to drive data ingestion and integration for AI/ML initiatives. You will lead design, implementation, and governance of scalable pipelines and collaborate with data scientists, architects, and product partners to enable Generative AI and RAG-enabled solutions.
You will mentor engineers, own technical outcomes, and push for automation, security, and measurable improvements in data platform capabilities and operational resilience.
Qualifications
- 8-10 years in Data Engineering or related analytics disciplines.
- 3+ years as a Technical Delivery leader.
- Experience with enterprise data integration, warehousing, ETL/ELT, cloud data platforms.
- Strong SQL, Python, and experience with large-scale datasets.
- Experience with Power BI, Tableau, Python, Azure Data Factory, Azure AI Search.
- Familiarity with Responsible AI, Model Risk Management is a plus.
Responsibilities
- Lead data analysis, integration, governance, and AI-enabled data pipelines for AI/ML and GenAI apps.
- Partner with Product Owners, Data Engineers, Data Scientists, and business stakeholders to translate needs into data requirements and models.
- Design and implement scalable ingestion and integration pipelines for structured, semi-structured, unstructured data.
- Ensure governance, security, lineage and observability across pipelines.
- Mentor engineers, manage ambiguity, and drive platform capability improvements.
- Lead cross-functional initiatives from discovery through production and measure outcomes.
Skills
Education
Tools
Job description
Application Deadline:
09/29/2026
Address:
320 S Canal Street
Job Family Group:
Data Analytics & Reporting
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.
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.
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 improvement initiatives that increase automation, operational efficiency, and scalability of data ingestion and integration processes.
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
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.
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.
$122,400.00 - $228,000.00
Salaried
The above represents BMO Financial Group’s pay range and type.
Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.
BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans.
To view more details of our benefits, please visit: https://jobs.bmo.com/global/en/Total-Rewards
- Performance-based incentives
- Discretionary bonuses
- Other perks and rewards
- Health insurance
- Tuition reimbursement
- Accident and life insurance
- Retirement savings plans
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 http://jobs.bmo.com/us/en
BMO is proud to be an equal employment opportunity employer. We evaluate applicants without regard to race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or any other legally protected characteristics. We also consider applicants with criminal histories, consistent with applicable federal, state and local law.
BMO is committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please send an e-mail to BMOCareers.Support@bmo.com and let us know the nature of your request and your contact information.
BMO does not accept unsolicited resumes from any source other than directly from a candidate. Any unsolicited resumes sent to BMO, directly or indirectly, will be considered BMO property. BMO will not pay a fee for any placement resulting from the receipt of an unsolicited resume. A recruiting agency must first have a valid, written and fully executed agency agreement contract for service to submit resumes.
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