Required Experience/Skills

• 5+ years of hands-on experience with IBM DataStage, including the design, development, and optimization of complex ETL workflows.
• Strong knowledge of IBM Cloud Pak for Data and experience working with cloud platforms such as AWS, Azure, or Google Cloud.
• Proven experience in platform and infrastructure engineering, with a focus on cloud-native environments.
• Solid background in data engineering and ETL practices, with expertise in SQL and NoSQL database technologies.
• Proficiency in programming/scripting languages such as Python, Java, or Shell, especially for data-related operations and automation.
• Familiarity with DevOps tools and practices, including Jenkins, Docker, Kubernetes, and infrastructure-as-code tools like Terraform.
• Understanding of data security, privacy, governance, and compliance standards (e.g., GDPR, HIPAA).
• Strong analytical, troubleshooting, and problem-solving capabilities.
• Excellent communication and collaboration skills, with the ability to work effectively across technical and non-technical teams.

Roles & Responsibilities

• Design, develop, and maintain DataStage ETL pipelines to support scalable and efficient data processing.
• Work closely with Cloud Pak for Data services to architect and deploy secure, scalable, and resilient data solutions.
• Manage and optimize cloud-based infrastructure supporting data pipelines and processing tasks.
• Drive best practices in data engineering, including CI/CD, version control, and automation, across hybrid cloud environments.
• Integrate and support a modern data platform by leveraging tools and technologies across the data engineering stack.
• Build and maintain DevOps pipelines for automating deployments and data operations in the cloud.
• Ensure all solutions adhere to data governance, security, and compliance frameworks.
• Proactively monitor, troubleshoot, and resolve issues in ETL processes and infrastructure components.
• Collaborate with data scientists, architects, engineers, and business stakeholders to develop data solutions that meet organizational objectives.

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