Datastage Platform Engineer

Overview

Remote
Depends on Experience
Full Time

Skills

Datastage
cloud pak
ETL

Job Details

Role: Datastage Platform Engineer
Location: Remote
Type: Fulltime

Job Description

  • 3-5 years of experience in DataStage, including designing, developing, and optimizing ETL workflows.
  • Strong experience with Cloud Pak for Data (IBM) and cloud-based platforms such as AWS, Azure, or Google Cloud.
  • Proven experience in platform and infrastructure engineering, particularly in cloud environments.
  • Expertise in Data Engineering and ETL processes with a strong background in database technologies (SQL, NoSQL).
  • Proficiency in programming languages such as Python, Java, or Shell scripting for data operations.
  • Familiarity with DevOps practices, tools (e.g., Jenkins, Docker, Kubernetes), and infrastructure automation (e.g., Terraform).
  • Knowledge of data security, governance, and compliance best practices.
  • Strong troubleshooting, problem-solving, and analytical skills.
  • Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams.
  • Design, develop, and optimize DataStage ETL workflows for large-scale data processing tasks.
  • Collaborate with Cloud Pak services to architect and deploy data solutions, ensuring scalability, security, and high availability.
  • Participate in the cloud infrastructure management for data pipelines using various cloud services, ensuring efficiency and cost optimization.
  • Provide expertise in data engineering best practices for integrating and managing data on hybrid cloud platforms (e.g., IBM Cloud Pak for Data).
  • Build and support data platforms by integrating tools and technologies across the data engineering stack.
  • Design and implement DevOps pipelines for automation of data operations in cloud environments.
  • Ensure data governance, security, and compliance standards are maintained throughout the engineering lifecycle.
  • Troubleshoot, diagnose, and resolve any issues with data processing pipelines or infrastructure components.
  • Collaborate with cross-functional teams, including data scientists, architects, and business stakeholders, to optimize data solutions.
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