Senior Data Engineer, AI Experience at Scotiabank
Toronto, ON M5H 1H1, Canada -
Full Time


Start Date

Immediate

Expiry Date

19 Nov, 25

Salary

0.0

Posted On

20 Aug, 25

Experience

7 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Privacy Regulations, Data Modeling, Strategic Thinking, Data Quality, Data Architecture

Industry

Banking/Mortgage

Description

Requisition ID: 227526
Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.
Join Scotiabank’s purpose-driven, high-performing team where innovation meets impact. We are looking for a Senior Data Engineer, AI Experience with a proven track record of Spring and Python who have worked with agentic AI and data engineering and have deployed cloud-native applications in production.

QUALIFICATIONS

  • 7+ years of proven leadership in designing data pipelines and real-time systems.
  • Strong architectural skills in data modeling and warehouse integrations.
  • Experience driving adoption of IaC and CI/CD best practices.
  • Expertise in data quality, lineage, and cataloging.
  • Knowledge of privacy regulations (GDPR, PIPEDA).
  • Ability to partner with AI/ML teams for RAG and agentic systems.
  • Proficiency in platform observability and cloud deployments.
  • Strategic thinking in governance frameworks and data architecture.
  • Mentorship and thought leadership experience.

How To Apply:

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Responsibilities
  • Architect and implement scalable batch and streaming pipelines for structured and unstructured data.
  • Design and maintain dimensional and analytical data models to support ML workflows, including LLM training and inference.
  • Lead ELT orchestration using Airflow and DBT for complex AI/ML pipelines.
  • Integrate and manage data across BigQuery, Delta Lake, and Databricks environments.
  • Collaborate with AI/ML teams to version, serve, and monitor datasets for fine-tuning and inference.
  • Build and optimize feature pipelines and data services for RAG-based and agentic AI systems.
  • Champion CI/CD, observability, and monitoring best practices for data infrastructure.
  • Ensure secure, compliant, and scalable data platforms across Azure and GCP.
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