Cloud Data Engineer (Snowflake/Databricks) in Canada Creek, Nova Scotia at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Cloud Data Engineer (Snowflake/Databricks) based in Canada.

This role offers the opportunity to design and optimize modern, cloud-based data platforms that support analytics and business intelligence at scale. You’ll build reliable data pipelines, develop transformation workflows, and create efficient data models using technologies such as Snowflake and Databricks. Working closely with analytics, BI, and engineering teams, you’ll help ensure data is accurate, secure, performant, and readily available. You’ll have ownership across the data engineering lifecycle, from ingestion and transformation to orchestration, monitoring, and optimization. The role combines hands-on engineering with cross-functional collaboration in a modern cloud environment. It is well suited to a data engineer who enjoys solving complex data challenges and building production-grade infrastructure.

• Design, develop, and maintain scalable ETL/ELT data pipelines that reliably process and transform large volumes of data.
• Build, optimize, and maintain data transformation workflows using Snowflake and/or Databricks.
• Develop effective data modeling strategies, including star schemas, lakehouse architectures, and other scalable approaches.
• Optimize query performance, data processing efficiency, and cloud infrastructure costs.
• Implement and maintain workflow orchestration using Airflow or comparable orchestration technologies.
• Develop reliable datasets and data products that support analytics, business intelligence, and reporting teams.
• Establish and maintain data quality, governance, monitoring, and reliability practices across data pipelines and platforms.
• Work with batch and streaming data processing technologies to support evolving data requirements.
• Collaborate with analytics, BI, engineering, and other cross-functional stakeholders to understand requirements and deliver effective data solutions.
• Continuously improve data platform architecture, pipeline reliability, performance, scalability, and operational efficiency.

• 4+ years of professional experience in Data Engineering or a closely related field.
• Strong proficiency in SQL and Python, with experience applying both to production-grade data engineering solutions.
• Hands-on experience working with Snowflake and/or Databricks.
• Practical experience with Apache Spark, including batch and/or streaming data processing.
• Proven experience designing and implementing ETL/ELT pipelines.
• Familiarity with Apache Airflow or similar data orchestration technologies.
• Experience working with at least one major cloud platform, including AWS, Azure, or Google Cloud Platform (GCP).
• Strong understanding of data modeling principles and experience designing scalable analytical data structures.
• Knowledge of data quality, governance, monitoring, and reliability practices.
• Experience with dbt or comparable data transformation tools is preferred.
• Experience with real-time streaming technologies such as Kafka, Kinesis, or Pub/Sub is an advantage.
• Familiarity with BI tools and downstream analytics use cases is a plus.
• Strong analytical and problem-solving abilities, with the capacity to work effectively across technical and business requirements.
• Strong communication and collaboration skills, with the ability to work effectively with engineering, analytics, and BI stakeholders.

• Full-time opportunity within a modern cloud data engineering environment.
• Fully remote work arrangement.
• Opportunity to work extensively with Snowflake, Databricks, Spark, SQL, Python, and cloud technologies.
• Exposure to modern ETL/ELT, data modeling, orchestration, transformation, and analytics architectures.
• Opportunity to contribute to scalable data platforms supporting advanced analytics and business intelligence.
• Cross-functional collaboration with data engineering, analytics, BI, and technology teams.
• Opportunity to develop expertise across AWS, Azure, and/or GCP cloud environments.
• Hands-on experience with modern data engineering practices and technologies.