Data Engineer

Zafin•Toronto, ON

2d•CA$85,000 - CA$135,000•Hybrid

About The Position

Zafin is an AI platform company helping regulated institutions modernize how critical work is designed, governed, and delivered. Our technology enables organizations to move faster while maintaining the governance, accountability, and control required in highly regulated environments. Our portfolio includes Zafin AIOS, an agent orchestration platform for governed AI work; the Zafin Banking Platform, which helps banks modernize product, pricing, offers, billing, loyalty, and relationship management; and Zafin IO, an integration platform that connects data, systems, and workflows across complex enterprise environments. Headquartered in Toronto, Canada, Zafin partners with leading financial institutions across North America, Europe, the Middle East, Africa, and Asia-Pacific. As AI transforms the future of financial services, we're building the platforms that help regulated organizations adopt AI responsibly and at scale. We are seeking a highly skilled Data Engineer II to design, build, and scale robust data platforms that power analytics and product use cases. This role requires strong ownership in developing data pipelines, optimizing infrastructure, and ensuring high standards of data quality, governance, and performance. The ideal candidate brings deep hands-on experience with modern data technologies and thrives in a fast-paced, product-driven environment.

Requirements

5–8 years of experience in data engineering or a related field.
Strong programming skills in Python, Scala, or Java.
Advanced proficiency in SQL and data modeling concepts.
Hands-on experience with: Apache Spark (distributed data processing)
Snowflake or similar cloud data warehouses
Kafka or other streaming platforms
Airflow or similar orchestration tools
Kubernetes (container orchestration)
Proven experience building both batch and real-time data pipelines.

Responsibilities

Design, build, and maintain scalable data pipelines for ingesting, transforming, and loading data across batch and real-time (streaming) systems.
Integrate data from diverse sources while ensuring reliability, scalability, and efficiency.
Design and implement optimized data models, warehouse schemas, and tables for analytics and reporting.
Apply best practices in data architecture to support scalable data consumption.
Manage and optimize data platforms and infrastructure including Spark, Snowflake, Kafka, Airflow, and Kubernetes.
Ensure high availability, scalability, and fault tolerance of data systems.
Implement data validation frameworks, automated testing, and monitoring systems.
Establish alerting mechanisms to ensure data freshness, accuracy, and completeness.
Enforce data governance standards including access controls, data lineage, and compliance.
Ensure secure handling of sensitive data (PII) aligned with regulatory requirements.
Optimize queries, partitioning strategies, file formats, and compute resources.
Continuously improve system performance while managing infrastructure costs.
Develop and maintain CI/CD pipelines for data workflows.
Build internal tools, reusable frameworks, and automation solutions to improve engineering productivity.