Role & responsibilities

• Design, build, and maintain data pipelines using Scala, Spark, and SQL.
• Develop and optimize data transformations using DataFrames.
• Manage and maintain our data infrastructure on cloud platforms (preferably AWS or GCP).
• Implement and manage data streaming solutions using MSK (Managed Streaming for Kafka) or Kafka.
• Design and implement data storage solutions using S3 or similar object storage.
• Automate infrastructure provisioning, configuration management, and data pipeline deployments using tools such as Terraform, Ansible, or CloudFormation.
• Build and maintain CI/CD pipelines for automated data pipeline deployments using tools such as Jenkins, GitLab CI, or CircleCI.
• Monitor data pipeline performance and identify areas for optimization.
• Troubleshoot and resolve data pipeline and infrastructure issues in a timely manner.
• Implement data quality checks and monitoring.
• Implement security best practices across our data infrastructure and pipelines.
• Collaborate with data scientists and data engineers to understand their data needs and build solutions to meet those needs.
• Participate in on-call rotations to ensure 24/7 system availability.
• Contribute to the development of internal tools and automation scripts.
• Stay up-to-date with the latest data engineering and DevOps technologies and trends.

Preferred candidate profile

• Bachelor's degree in Computer Science, Data Science, or a related field (or equivalent experience).
• 5+ years of experience in a data engineering or DevOps role.
• Strong proficiency in Scala, Spark, and SQL.
• Experience with DataFrames.
• Experience with cloud platforms (preferably AWS or GCP).
• Experience with MSK (Managed Streaming for Kafka) or Kafka.
• Experience with S3 or similar object storage.
• Experience with infrastructure-as-code tools such as Terraform or CloudFormation.
• Experience with configuration management tools such as Ansible or Chef.
• Experience with CI/CD tools such as Jenkins, GitLab CI, or CircleCI.
• Proficiency in scripting languages such as Python or Bash.
• Strong understanding of data warehousing concepts.
• Strong troubleshooting and problem-solving skills.
• Excellent communication and collaboration skills.