ABOUT REDCLOUD

We are revolutionizing B2B commerce by delivering scalable, innovative solutions that empower businesses to grow and thrive in a competitive landscape. Our AI powered platform streamlines logistics, payments, and supply chain operations, making it easier for companies to connect, transact, and succeed. With a diverse and dynamic team, we’re committed to driving impact, fostering collaboration, and shaping the future of global trade.

REQUIREMENTS:

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field. Master’s degree is a plus.
• 3+ years of experience in DataOps, Data Engineering, or a related role.
• Proven experience with data pipeline tools (Airflow).
• Hands-on experience with cloud platforms (AWS, Azure, Google Cloud) and data services (Snowflake).
• Proficiency in SQL and experience with relational databases.
• Strong programming skills in Python.
• Experience with IaC and CI/CD tools (Terraform, Jenkins, GitHub, Concourse CI).
• Knowledge of data warehousing and ETL/ELT processes.

SOFT SKILLS:

• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.
• Ability to work independently and as part of a team.
• Attention to detail and a commitment to data quality.

THE ROLE:

The DataOps Engineer is responsible for the development, implementation, and maintenance of a data platform serving the company’s data lake: data acquisition, transformation pipelines, presentation, data access and supporting infrastructure. This role focuses on optimizing data flow and collection for cross-functional teams. The ideal candidate has a deep understanding of data engineering, its principles and best practices, DevOps and software development principles, and is committed to enhancing data management processes.

KEY RESPONSIBILITIES:

• Pipeline Development & Management:
• Design, build, and maintain scalable and reliable data pipelines.
• Automate data ingestion, transformation, and delivery processes.
• Ensure data quality, integrity, and reliability throughout the data lifecycle.
• Infrastructure Management:
• Manage cloud data infrastructure.
• Implement and optimize data storage solutions.
• Monitor and ensure the performance, scalability, and security of data platforms.
• Setting up alerts to detect anomalies
• Collaboration & Support:
• Collaborate with data scientists, analysts, Engineers and other stakeholders to understand data requirements and deliver optimal solutions.
• Provide support for data-related issues and troubleshooting.
• Assist in the development and implementation of data governance and security policies including access management.
• Continuous Improvement:
• Implement CI/CD processes for data workflows.
• Optimize existing processes and frameworks for better performance and cost-efficiency.
• Stay updated with the latest industry trends and technologies to continuously improve data operations.