Senior Data Engineer - Corporate Data Analytics Group

Canadian Bank Note Company

6h•Remote

About The Position

The Senior Cloud Data Engineer is responsible for designing, building, and operating scalable, secure, and reliable cloud data platforms that power analytics, AI, and decision‑making across the organization. This role focuses on engineering high‑quality, production‑grade data pipelines and data products using Microsoft Azure, Microsoft Fabric, and Databricks, enabling downstream BI, Copilot, and AI/ML workloads.

Requirements

Legally eligible to work in Canada.
Able to obtain Government of Canada Reliability or Secret security clearance.
Fluent in English (speak, read, write).
Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, or a related field.
Strong understanding of lakehouse, data warehouse, and medallion architecture patterns.
5+ years of professional experience in cloud data engineering or data platform roles.
5+ years of hands‑on experience with Microsoft Azure data services.
5+ years of experience building data pipelines using SQL and Python or PySpark.
Working with structured and semi‑structured data.
Collaborating with analytics, BI, and AI teams.

Nice To Haves

10+ years of relevant professional experience
Microsoft Certifications: Fabric Analytics Engineer Associate (DP‑700) or Databricks Certified Data Engineer Associate or equivalent
Knowledge of AI/ML data requirements and feature engineering.
Experience with Microsoft Fabric (OneLake, lakehouses, pipelines, notebooks).
Experience with Azure Databricks for large‑scale data processing.
Experience with DataOps and CI/CD for data platforms.
Experience in manufacturing, software, or regulated environments.

Responsibilities

Design, build, and maintain scalable cloud‑native data pipelines supporting batch and near‑real‑time use cases.
Engineer and optimize data ingestion, transformation, and orchestration pipelines using Azure Data Factory, Microsoft Fabric, Databricks, and related services.
Implement and maintain lakehouse and medallion architectures (bronze, silver, gold) to support analytics and AI workloads.
Develop robust transformation logic using SQL, PySpark, and Python with a focus on performance and maintainability.
Partner with Analytics/BI Engineers to ensure datasets are optimized for semantic layers and AI consumption.
Collaborate with data scientists to support feature engineering and model training workloads.
Implement data quality, validation, lineage, and observability solutions to ensure trust in data assets.
Design and enforce data security practices including access controls, encryption, and data classification.
Automate deployment and operations using DataOps and infrastructure‑as‑code practices.
Optimize cloud performance and costs across the data platform.
Create and maintain technical documentation, runbooks, and architecture diagrams.
Provide technical leadership and mentorship to junior data engineers.