Data Platform Engineer - DevOps (Hybrid, Databricks/AWS)

Tecsys Inc.

Montreal (administrative region)

Hybrid

CAD 90,000 - 140,000

Full time

11 days ago

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Job summary

Tecsys Inc. is seeking a Data Platform Engineer (DevOps) to join the TecsysIQ Data & AI team. This hybrid role powers our analytics platform on Databricks and AWS, owning infrastructure-as-code, CI/CD, and data pipelines.

You will deploy and operate Databricks jobs and streaming pipelines, collaborate with data platform developers and product owners, and evolve release processes to scale across environments.

Qualifications

  • Hands-on experience with Databricks and Apache Spark, including building batch or streaming data pipelines.
  • Strong Terraform skills and comfort managing cloud infrastructure on AWS.
  • Working knowledge of Kubernetes and Helm, and containerization with Docker.
  • Proficiency in Python (PySpark) and/or Java, plus strong SQL for data validation.

Responsibilities

  • Provision and evolve AWS and Databricks accounts and workspaces using Terraform across regions and environments.
  • Own Helm charts and Kubernetes Jobs to onboard new environments and deploy the app layer.
  • Build and maintain Docker images and the Java/Maven build pipeline, publishing artifacts.
  • Integrate build, test, and deployment into CI/CD pipelines and improve release management.
  • Manage secrets and credentials safely through cloud secret management and Kubernetes.

Skills

Databricks
Apache Spark
Python (PySpark)
Java
SQL
CI/CD
Git workflows
Agile
Data pipelines
Kuberenetes
Terraform
Cloud (AWS)

Tools

Terraform
AWS
Kubernetes
Helm
Docker

Job description

Tecsys Inc. is seeking a Data Platform Engineer (DevOps) to join the TecsysIQ Data & AI team. This hybrid role powers our analytics platform on Databricks and AWS, owning infrastructure-as-code, CI/CD, and data pipelines.

You will deploy and operate Databricks jobs and streaming pipelines, collaborate with data platform developers and product owners, and evolve release processes to scale across environments.

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