Requirements

Expected technologies

Snowflake Data Cloud

etl

Apache Airflow

Kafka

AWS

Python

SQL

talend

Our requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field
• Minimum 4 years of experience in data engineering or data platform development
• Proven hands-on expertise in Snowflake, including performance tuning, data modeling, and advanced SQL
• Proficiency in SQL and scripting languages such as Python or Scala
• Experience with ETL/ELT frameworks and orchestration tools (e.g., dbt, Airflow, Talend)
• Familiarity with cloud platforms such as AWS, Azure, or GCP
• Strong understanding of data warehousing concepts, star/snowflake schemas, and data normalization/denormalization
• Experience working in Agile environments with tools like Jira and Confluence

Optional

• Snowflake certifications (e.g., SnowPro Core, SnowPro Advanced)
• Understanding of data governance and privacy regulations such as GDPR, HIPAA
• Exposure to machine learning workflows and streaming technologies like Kafka or Kinesis
• Experience with CI/CD practices, version control systems (Git), and infrastructure-as-code tools like Terraform

Your responsibilities

• Design, implement, and optimize Snowflake data pipelines to meet analytical and business intelligence needs
• Develop and maintain ELT/ETL workflows using tools such as dbt, Apache Airflow, Matillion, or equivalent
• Model and manage data warehouse and data lake solutions with an emphasis on dimensional modeling and data partitioning best practices
• Build and maintain secure, governed data environments, including the enforcement of access control and role-based policies
• Monitor and optimize pipeline performance, ensuring cost-efficiency and high availability
• Collaborate with cross-functional teams to gather requirements and translate them into scalable data solutions
• Integrate Snowflake with external data sources such as AWS S3, Azure Data Lake, Kafka, and REST APIs
• Troubleshoot data pipeline issues, ensuring data quality, lineage, and consistency
• Automate and manage CI/CD pipelines for deploying and maintaining data infrastructure
• Stay current with Snowflake features and industry best practices in modern data engineering