Snowflake Data Engineer (Energy sector project)

As part of a Data & Analytics team, you will help organizations solve complex business challenges by designing and delivering modern data solutions. You will work with large-scale data platforms, cloud technologies, and advanced analytics environments to extract business value from data across multiple industries.

• Strong hands-on experience in Data Engineering and Data Warehousing.
• Proven development experience using Snowflake.
• Knowledge of Snowflake data modelling including databases, schemas, roles, permissions, and security policies.
• Experience with Snowflake Tasks, Streams, and Stages.
• Strong SQL skills with large datasets.
• Experience with ETL/ELT development and data integration.
• Experience with cloud data platforms and distributed processing.
• Knowledge of programming languages such as Python, Java, or Scala.
• Understanding of data formats like Parquet, ORC, and Avro.
• Experience in Agile delivery methodologies.
• Strong communication and problem-solving skills.

Nice to have:

• Experience with Snowflake performance tuning and resource management.
• Knowledge of data integration tools such as dbt, Matillion, Palantir, or Informatica.
• Experience with streaming technologies like Kafka or NiFi.
• Experience with cloud services: AWS S3, AWS Lambda, AWS Glue, AWS EMR.
• Knowledge of DevOps practices and containerization.
• Experience working directly with clients and stakeholders.

• Design, develop, and deploy scalable Snowflake data pipelines in cloud environments.
• Develop and optimize ETL/ELT processes including migration of on-premise data workflows to cloud platforms.
• Design and implement Snowflake data models including schemas, databases, roles, and security policies.
• Build and maintain Snowflake objects such as Tasks, Streams, and Stages.
• Optimize SQL queries, data models, and workflows for performance and scalability.
• Develop batch and real-time data processing for structured and unstructured data.
• Work with data integration tools and modern data engineering frameworks.
• Collaborate with stakeholders to translate requirements into technical solutions.
• Participate in full data solution lifecycle: analysis, design, development, testing, deployment, and support.
• Contribute to DevOps practices, automation, and deployment processes.