Data Engineer - Cloud Platform

IVARI•Toronto, ON

4d•Hybrid

About The Position

We are seeking a Data Engineer – Cloud Platform to contribute hands-on to the delivery and operation of Sagicor’s Azure-based enterprise data platform. The role focuses on building, operating, and supporting data pipelines and platform components within established architectural standards and governance frameworks. Reporting to the Principal Engineer, Data & AI Platforms, this role will be based in one of our Toronto offices on a hybrid basis. This role is well-suited to a mid-level data engineer looking to deepen hands-on experience in cloud data platforms within an enterprise environment.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or a related field.
2–4 years of hands‑on experience in data engineering or analytics platform roles.
Strong SQL and Python skills.
Experience with Azure Data Factory or Synapse Pipelines.
Hands-on experience with Azure Data Lake Storage.
Experience using Azure Synapse Analytics.
Experience with Azure Databricks / Apache Spark.
Understanding of data modeling concepts and analytics-oriented schemas.
Experience with version control (Git) and CI/CD concepts.
Work experience with data integration technologies such as SQL Server Integration Services (SSIS) and BizTalk.
Familiar with consuming and integrating RESTful APIs.

Responsibilities

Design, build, and operate data ingestion and transformation pipelines on Microsoft Azure.
Support and maintain enterprise data lake, warehouse, and lakehouse solutions using Azure services.
Monitor data pipelines, troubleshoot issues, and support production incidents.
Participate in operational support activities, including scheduled monitoring, incident response, and pipeline health reviews.
Develop analytics-ready datasets for reporting, regulatory, and analytics use cases.
Apply data quality checks, access controls, and governance requirements.
Collaborate with senior engineers, analysts, and business teams to understand data and reporting requirements.
Contribute to CI/CD pipelines and Infrastructure-as-Code practices.
Support AI and machine learning initiatives through data preparation and pipeline enablement.