Cloud Data Platform Engineer

Position Overview

• The Cloud Data Platform Engineer is responsible for designing, implementing, operating, and evolving a scalable, secure, and high-performance Cloud data platform for various organizational needs.
• This role involves working closely with cross-functional teams, collaboration, innovation, and continuous improvement.

Key Responsibilities

• Platform Engineering & Development
  • Design, implement, operate, and continuously monitor cloud-based data platform services and infrastructure supporting enterprise data services, and so architectures and technologies including Snowflake, Snowpark, Azure and AWS.
  • Support and lead efforts to implement solutions by developing standardized including developing standardized processes for platform solutions.
  • Engineer reusable platform services, templates, and automation patterns that accelerate development production and platform adoption.
  • Implement platform services with enterprise capabilities such as, and related technologies.
• Cloud Infrastructure and Automation
  • Build and maintain scalable, resilient, and high-performing data platform cloud environments and infrastructure to support platform services.
  • Implement CI/CD, automation as Code (IaC), and infrastructure management practices using enterprise platform automation and deployment tools GitHub, Azure DevOps, Terraform, and related technologies.
  • Design and maintain cloud platform orchestrations as Code services, deployment processes, and pattern templates to ensure standardization and efficiency.
  • Develop platform services, automation, and documentation that enable self-service provisioning, monitoring, telemetry and centralized logging and alerting.
  • Partner with application teams to provide data platform services with application development, testing, and operational services.
  • Provide technical expertise in designing and building: data architecture and solutions, collaborated with architects and engineers within our cloud platform delivery, stage governance solutions for data solutions, platforms, and applications designed for ease to maintain them with enhanced security and efficient execution.
  • Monitor, plan, engineer, and contribute to best practices in cloud practices of data development.
• Collaboration, Innovation, and Continuous Improvement
  • Follows emerging cloud platform services and modern adoptable cloud enterprise systems.
  • Provides technical expertise in designing and best data architecture and solutions, Collaborates with architects and engineers within our cloud platform delivery, stage, governance solutions for data solutions.
  • Partner with data scientists, and BIs, and applications developers to maintain them with enhanced security and efficient execution.
  • Monitor, plan, engineer and contribute to best practice in cloud practices of data development.

Qualifications

• Education & Experience
  • Bachelor's degree in computer science, Data Engineering, or related Master's preferred.
  • 7+ years of experience in data structures, Data Engineering, or Data Pipeline development.
  • Hands-on experience with at least one major cloud infrastructure (Azure, AWS).
  • Strong experiences with Cloud Platform tools like Snowflakes and Snowpark.
  • Proven experience in Cloud engineering including but not limited to infrastructure environments, Data design (storage and other data tools).
  • Proven experience with other SAAS & services.
  • Must have familiarity in building data pipelines with tools like Data Factory, Apache, Databricks, Helm, Kafka, or similar.
• Skills
  • Proficiency in Infrastructure-as-code (GitHub, Citadel, Terraform, Junit, Azure Devops, & data modeling).
  • Strong SQL experience - standard query language – data model.
  • Proficiency in Automation as code (GitHub, Crawler, Terraform, unit, Azure, Devops, & tool).

Key Success Metrics

• Improve performance, scalability, reliability of the cloud platform
• Drive platform re-use, accessibility by business and analytics consumers
• Efficiency model of data pipeline and processing
• Adapted and Performance of automation, Infrastructure as Code, and Self-service platform capabilities
• Cost optimization and resource utilization of cloud data
• Automation and satisfaction