Data Engineer

Cyber Space Technologies LLC; Montreal (administrative region); Hybrid; CAD 90,000 - 140,000; Full time

Job summary

Cyber Space Technologies LLC is seeking a seasoned Data Engineer to design, build, and maintain scalable data pipelines and platforms. The role centers on Python, SQL, ETL/ELT, cloud tech, and modern data engineering frameworks.

You will collaborate with data scientists, analysts, and stakeholders to ensure high‑quality, available data and robust data solutions. Montreal‑hybrid work arrangement is expected.

Qualifications

• 5+ years of experience in Data Engineering or related field.
• Strong hands‑on experience with Python and SQL.
• Experience with data warehouses and/or data lakes.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience with Spark/PySpark and modern data processing technologies.
• Experience with orchestration tools such as Airflow, or Azure Data Factory.
• Strong data integration and API ingestion.
• Git and CI/CD practices.

Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes.
• Build data engineering solutions using Python and SQL.
• Develop and optimize data ingestion, transformation, and processing workflows.
• Work with structured and unstructured data from multiple sources.
• Maintain data models, data warehouses, and data lakes.
• Implement data quality, monitoring, and performance optimization.
• Collaborate with Data Scientists, Analysts, Developers, and business teams.
• Troubleshoot data pipeline failures and resolve performance issues.

Skills

Python

SQL

ETL/ELT

Data modeling

Data pipelines

Cloud platforms

Spark/PySpark

Airflow

APIs ingestion

Git & CI/CD

Tools

Databricks

Snowflake

Microsoft Fabric

Docker

Kubernetes

Job description

We are seeking an experienced Data Engineer to design, develop, and maintain scalable data pipelines and data platforms. The ideal candidate will have strong hands‑on experience with Python, SQL, ETL/ELT, cloud technologies, and modern data engineering frameworks.

The candidate will work closely with data analysts, data scientists, application teams, and business stakeholders to build reliable data solutions and ensure high‑quality data availability.

Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes.
• Develop data engineering solutions using Python and SQL.
• Build and optimize data ingestion, transformation, and processing workflows.
• Work with structured and unstructured data from multiple sources.
• Develop and maintain data models, data warehouses, and data lakes.
• Implement data quality, validation, monitoring, and performance optimization.
• Integrate data from APIs, databases, applications, and third‑party sources.
• Collaborate with Data Scientists, Analysts, Developers, and business teams.
• Troubleshoot data pipeline failures and resolve performance issues.
• Follow best practices for data security, governance, and documentation.
• Participate in Agile ceremonies and contribute to technical design and architecture discussions.

Required Skills

• 5+ years of experience in Data Engineering or a related field.
• Strong hands‑on experience with Python and SQL.
• Strong knowledge of relational databases and data modeling.
• Experience with data warehouses and/or data lakes.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience with modern data processing technologies such as Spark / PySpark.
• Experience with orchestration tools such as Airflow, Azure Data Factory, or equivalent.
• Strong understanding of data integration and API‑based data ingestion.
• Experience with Git and CI/CD practices.
• Strong analytical, troubleshooting, and communication skills.

Preferred Skills

• Experience with Databricks, Snowflake, or Microsoft Fabric.
• Experience with Kafka or other streaming technologies.
• Knowledge of Docker/Kubernetes.
• Experience with data governance and data quality frameworks.
• Experience working in an Agile/Scrum environment.
• Relevant cloud or data engineering certifications.

Work Location

Candidates should be comfortable working in a hybrid environment and meeting onsite requirements in Montreal.