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Data Architect - Cloud Analytics

Data Architect - Cloud Analytics

KRP HR SOLUTIONS PVT LTDIndia
5 days ago
Job description

Description :

We are looking for a seasoned Senior Data Architect with proven expertise in modern data engineering, distributed computing, and cloud analytics.

This role is ideal for professionals with strong Databricks experience and a consulting mindset someone who can design, optimize, and scale data platforms that deliver real business value across varied industries and use cases.

Key Responsibilities :

  • Architect and implement scalable data platforms using Databricks, Apache Spark, and modern cloud-native services.
  • Lead end-to-end solutioning for complex data initiatives in multi-cloud environments (AWS, Azure, or GCP).
  • Collaborate with clients and internal teams to understand requirements, define architecture, and guide development.
  • Build and optimize robust CI / CD pipelines to ensure scalable, secure, and production-grade deployments.
  • Design for performance and cost-efficiency, ensuring optimal use of compute and storage resources.
  • Mentor and guide data engineers, sharing best practices in cloud data architecture, Spark optimization, and MLOps workflows.
  • Stay current with Databricks innovations and integrate platform enhancements into active solutions.

Required Qualifications & Skills :

  • 10+ years of experience in technology consulting, with 7+ years in data engineering, data platforms, and analytics.
  • 6+ years of hands-on project experience using Databricks in enterprise environments.
  • Databricks Certified Data Engineer Professional (or must be willing to complete certification within 2 months of joining).
  • Deep expertise in at least one major cloud platform (AWS, Azure, or GCP) with working knowledge of at least one more.
  • Strong hands-on skills in Apache Spark, including understanding of runtime internals and performance tuning.
  • Experience in designing and maintaining CI / CD pipelines for data products.
  • Familiarity with ML Ops practices and the deployment of ML models in production environments.
  • Ability to assess and improve performance, scalability, and cost-efficiency across data workflows.
  • Excellent communication and client-facing skills with a consulting mindset.
  • (ref : hirist.tech)

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