Cloud Data Developer - Databricks

Canadian Natural•Calgary, AB

1d•Onsite

About The Position

At Canadian Natural, we are proud to be a leader in Canada’s energy industry. Join us and help drive innovation and long-term value through integrity, teamwork, and a passion for doing things right. Make your mark while you grow your career! Canadian Natural is seeking an experienced Data Developer to play a key role in advancing our enterprise Databricks Lakehouse platform. This role will replace a departing senior data developer and will provide technical leadership across the design, development, and operation of scalable data pipelines and curated data products supporting analytics, business intelligence, and data science. You will work closely with Data Analysts, Data Scientists, Business Systems Analysts, and platform teams to deliver production-grade, cost-efficient, and governed data solutions. This role sits within the Enterprise Data & Analytics team and contributes directly to our goal of enabling CNRL to operate as a data‑driven organization.

Requirements

Post‑secondary degree in Computer Science, Software Engineering, or equivalent practical experience.
12+ years of experience in data engineering, with strong hands-on Databricks experience.
Advanced proficiency in PySpark, Python, and SQL.
Proven experience building production-grade ETL/ELT pipelines at scale.
Strong understanding of Lakehouse architectures, Delta Lake, and distributed data processing.
Experience with cloud platforms (Azure preferred; AWS acceptable).
Hands-on experience with DevOps / CI/CD for data workloads.

Nice To Haves

Experience with MLOps workflows and deploying ML workloads in Databricks.
Familiarity with data integration tools such as HVR, Azure Data Factory, or similar.
Experience in regulated, enterprise environments.
Exposure to streaming architectures (e.g., Kafka, Structured Streaming).

Responsibilities

Lead the design and delivery of scalable Databricks data pipelines using Spark, Delta Lake, and PySpark to support analytics, BI, and machine learning use cases.
Drive Lakehouse architecture and technical direction across data ingestion, transformation, and curation, ensuring production‑grade, scalable data platforms.
Set technical standards and best practices for batch and streaming data engineering, influencing how data solutions are built across the platform.
Build and optimize high‑performance, cost‑efficient ETL/ELT pipelines on Databricks, with a strong focus on Spark tuning and workload optimization.
Implement enterprise‑grade data quality, governance, and security patterns, including Unity Catalog, access controls, and monitoring at scale.
Serve as a senior technical leader and mentor, collaborating with analytics, data science, and business teams to deliver trusted, high‑impact data products.