Data DevOps Engineer — Cloud, ML & Big Data

HSBC; Toronto; On-site; CAD 110,000 - 145,000; Full time

Job summary

HSBC in Toronto is seeking a Data DevOps Engineer to join the CTO Analytics Technology team. You will help optimize data workflows across multi‑cloud environments and collaborate with data scientists and engineers to deliver scalable data platforms.

Key responsibilities include building and maintaining data pipelines with Hadoop, BigQuery, EMR, Snowflake, Databricks; implementing ML workflows with Airflow, MLflow and Vertex AI; and deploying Python applications and APIs.

Qualifications

• Bachelor's degree in CS or related field or equivalent practical experience.
• Familiarity with Hadoop, HDFS, BigQuery, EMR, Snowflake and Databricks.
• Experience with JupyterHub, MLflow, Vertex AI, Dataproc, Dataflow and Airflow.
• Proficiency in Python including API/app development.
• Knowledge of Linux and cloud platforms (GCP/AWS/Azure).
• Understanding of Kubernetes, MLOps/LLMOps and LLMs is a plus.

Responsibilities

• Assist in development, deployment, and maintenance of data pipelines.
• Support data science initiatives with tools like JupyterHub and Vertex AI.
• Collaborate with data scientists to implement ML models and workflows.
• Develop Python applications and APIs to support data workflows.
• Monitor data pipelines for quality and reliability, troubleshoot as needed.
• Participate in ML model deployment, including MLOps/LLMOps practices.
• Work with Kubernetes to deploy apps in cloud environments.
• Stay updated on data engineering, DevOps and cloud trends.

Skills

Python programming

APIs development

Collaboration

Education

Bachelor's degree in Computer Science or related field

Tools

Hadoop

HDFS

BigQuery

EMR

Snowflake

Databricks

JupyterHub

MLflow

Vertex AI

Dataproc

Dataflow

Airflow

pandas

NumPy

PySpark

Python

GCP

AWS

Azure

Linux

Kubernetes

LLMOps

OpenAI

Job description

HSBC in Toronto is seeking a Data DevOps Engineer to join the CTO Analytics Technology team. You will help optimize data workflows across multi‑cloud environments and collaborate with data scientists and engineers to deliver scalable data platforms.

Key responsibilities include building and maintaining data pipelines with Hadoop, BigQuery, EMR, Snowflake, Databricks; implementing ML workflows with Airflow, MLflow and Vertex AI; and deploying Python applications and APIs.