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 to meet the organization's various 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 that support enterprise data services, as well as architectures and technologies such as Snowflake, Snowpark, Azure, and AWS.
  • Support and lead efforts to implement solutions by developing standardized processes for platform solutions.
  • Develop reusable platform services, templates, and automation patterns that accelerate development, production, and platform adoption.
  • Implement platform services with enterprise capabilities, such as [list of capabilities], and related technologies.
• Cloud Infrastructure and Automation
  • Build and maintain scalable, resilient, and high-performing cloud environments and infrastructure for data platforms to support platform services.
  • Implement CI/CD, Infrastructure as Code (IaC), and infrastructure management practices using enterprise platform automation and deployment tools such as 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 alongside application development, testing, and operational services.
  • Provide technical expertise in designing and building data architectures and solutions; collaborate with architects and engineers within our cloud platform delivery team to implement governance solutions for data solutions, platforms, and applications designed to be easy to maintain, with enhanced security and efficient execution.
  • Monitor, plan, engineer, and contribute to best practices in cloud-based data development.
• Collaboration, Innovation, and Continuous Improvement
  • Covers emerging cloud platform services and modern, adoptable cloud-based enterprise systems.
  • Provides technical expertise in designing optimal data architectures and solutions; collaborates with architects and engineers on our cloud platform delivery, staging, and governance solutions for data solutions.
  • Partner with data scientists, business intelligence (BI) professionals, and application developers to maintain them with enhanced security and efficient execution.
  • Monitor, plan, engineer, and contribute to best practices in cloud-based data development.

Qualifications

• Education & Experience
  • Bachelor's degree in computer science, data engineering, or a related master's degree is 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).
  • Extensive experience with cloud platform tools such as Snowflakes and Snowpark.
  • Proven experience in cloud engineering, including but not limited to infrastructure environments and data design (storage and other data tools).
  • Proven experience with other SaaS platforms and services.
  • Must be familiar with building data pipelines using tools such as Data Factory, Apache, Databricks, Helm, Kafka, or similar.
• Skills
  • Proficiency in Infrastructure-as-Code (GitHub, Citadel, Terraform, JUnit, Azure DevOps, and data modeling).
  • Extensive experience with SQL—standard query language and data models.
  • Proficiency in automation-as-code (GitHub, crawlers, Terraform, unit testing, Azure, DevOps, and related tools).

Key Success Metrics

• Improve the performance, scalability, and reliability of the cloud platform
• Drive platform reuse and accessibility for business and analytics users
• Efficiency Model for Data Pipelines and Processing
• Adaptation and Implementation of Automation, Infrastructure as Code, and Self-Service Platform Capabilities
• Cost Optimization and Resource Utilization of Cloud Data
• Automation and Satisfaction