Data DevOps Engineer Job Toronto area,Ontario Canada,IT/Tech

Our purpose – Opening up a world of opportunity – explains why we exist. Here at HSBC, we use our unique expertise, capabilities, breadth and perspectives to open new kinds of opportunity for our more than 40 million customers. We’re bringing together the people, ideas, and capital that nurture progress and growth, helping to create a better world – for our customers, our people, our investors, our communities, and the planet we all share.

In Canada, HSBC Global Services (Canada) Limited (HGCA) is a wholly owned subsidiary of HSBC Global Services Limited. Operating in Toronto and Vancouver, HGCA is part of a global service company, delivering services to support the operating entities of HSBC Group. We have different capabilities that provide tools and processes to facilitate the functions, business, and entities with their service management responsibilities.

Chief Technology Office (CTO) Data Technology is a data-driven shared service organization committed to unlocking value from data to serve our customers. We are a unified data technology team with a diverse organization of more than 3,000 colleagues across multiple global locations. We are transforming data technology into an Enterprise Shared Service Capability delivery model that will scale across key Global and Regional business markets.

Our CTO Analytics Technology team is at the forefront of developing cutting‑edge data science platforms across multi‑cloud environments. We leverage advanced analytics and data‑driven insights to enhance our services and drive business growth.

We are looking for a passionate and driven Data Development and Operations (Data Dev Ops) Engineer to join our CTO Analytics Technology team. In this role, you will work on a state‑of-the‑art data science platform, collaborating with data engineers, data scientists, and other stakeholders to optimize data workflows and enhance our analytics capabilities across multi‑cloud environments.

As our Data Dev Ops Engineer you will:

Assist in the development, deployment, and maintenance of data pipelines and workflows using Big Data technologies such as Apache Hadoop (Hadoop), Hadoop Distributed File System (HDFS), Google Big Query (Big Query), Amazon EMR (EMR), Snowflake, and Databricks
Support data science initiatives by utilizing tools such as Jupyter Hub, MLflow, Google Vertex AI (Vertex AI), Google Cloud Dataproc (Dataproc), Google Cloud Dataflow (Dataflow), and Apache Airflow (Airflow)
Collaborate with data scientists to implement machine learning models and workflows using libraries such as Tensor Flow and scikit‑learn
Utilize data manipulation libraries such as pandas, Num Py, and PySpark for data analysis and processing
Develop and maintain Python 3 (Python) applications and Application Programming Interfaces (APIs) to support data workflows and analytics
Monitor and troubleshoot data pipelines to ensure data quality and reliability
Participate in the deployment lifecycle of machine learning models and applications, including Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps) practices
Work with container orchestration tools such as Kubernetes (K8s) to manage and deploy applications in cloud environments
Stay updated with the latest trends and technologies in data engineering, Dev Ops, and cloud computing

You’ll likely have the following qualifications to succeed in this role:

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field (or equivalent practical experience)
Familiarity with Big Data technologies (Hadoop, HDFS, Big Query, EMR, Snowflake, Databricks)
Experience with data science tools (Jupyter Hub, MLflow, Vertex AI, Dataproc, Dataflow, Airflow)
Experience with data manipulation libraries (pandas, Num Py, PySpark)
Proficiency in Python, including experience in building APIs and applications
Familiarity with cloud platforms such as Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure (Azure), and their services
Basic knowledge of Linux operating systems
Understanding of Kubernetes (K8s), MLOps/LLMOps, and experience with Large Language Models (LLMs) such as OpenAI, Claude, or Gemini…