Data Engineer - Geoscience Data Platform

Location: Toronto, ON (Office-based, 2+ days/week)

About the Role

Join our team and play a pivotal role in designing, building, and maintaining scalable, secure, and high-performance data platforms for advanced analytics, data science, business intelligence, and AI workflows within geoscience data. You will significantly shape the technical direction, tooling, and best practices in data engineering and platform architecture from the ground up.

Responsibilities

• Design, build, and maintain cloud-based data platforms.
• Create and manage scalable data pipelines for ingesting, validating, storing, transforming, and processing data from various sources using batch and real-time methods.
• Migrate data from diverse sources into data warehouses or data lakes.
• Develop and maintain CI/CD pipelines and observability tools for automated deployments and testing.
• Consume, design, develop, publish, and support internal and external web APIs for geoscience data access.
• Develop and optimize data models to support analytical workloads.
• Provide reliable, well-structured datasets and semantic layers for BI tools, dashboards, advanced analytics, and AI/ML use cases.
• Ensure continuous monitoring of data platform health, performance, and availability.
• Provide operational tier 2/3 support in conjunction with the Cloud Ops team for data platform issues.
• Implement and enforce data quality standards and security best practices.
• Provide technical guidance and mentorship, establishing best practices and coding standards.
• Collaborate with stakeholders to align data engineering initiatives with business strategy.

Requirements

• Education: Bachelor’s (or Master’s) degree in Computer Science, Data Engineering, or a related field.
• Experience: 7+ years of experience in data platform, data engineering, or data architecture roles.
• Experience with enterprise-scale data platforms for managing structured, semi-structured, and unstructured data.
• Experience designing, building, and maintaining Data Lakes and/or Data Warehouses in production environments.
• Experience with relational and NoSQL data storage mechanisms.
• Experience with cloud platforms and data services (e.g., Databricks, Snowflake).
• Experience with streaming technologies (e.g., Kafka, Event Hub) and API integrations.
• Skills:
• Strong proficiency in SQL, Python, JSON, and distributed data processing frameworks (e.g., Spark).
• Knowledge of data modeling, ETL/ELT processes, and data warehousing concepts.
• Familiarity with containerization (e.g., Docker, Kubernetes), CI/CD pipelines, and infrastructure-as-code.
• Strong communication and stakeholder management abilities.

Work Environment

• Office-based working environment in our Toronto office, requiring attendance two or more days per week.