Job Description


Job Overview

  • Job ID:

    J52701

  • Job Title:

    Azure Cloud Architect

  • Location:

    Princeton,NJ

  • Duration:

    24 Months + Extension

  • Hourly Rate:

    Depending on Experience (DOE)

  • Work Authorization:

    US Citizen, Green Card, OPT-EAD, CPT, H-1B,
    H4-EAD, L2-EAD, GC-EAD

  • Client:

    To Be Discussed Later

  • Employment Type:

    W-2


Job Details

Job Title: Azure Cloud Architect
 
  • Contribute to the design and implementation of scalable data architectures, such as a Lakehouse, using Delta Lake and Unity Catalog.
  • Manage and maintain the underlying data infrastructure, which typically exists on a major cloud platform like AWS, Azure, or Google Cloud Platform.
  • Implement data governance practices, including data lineage, metadata management, and access controls integrating with other 3rd party products like Immuta, Protegrity for tokenization etc.
  • Adhere to software engineering best practices, including participating in code reviews and CI/CD (Continuous Integration/Continuous Deployment) automation.
  • Stay up to date with the latest trends and technologies in data engineering and the Databricks ecosystem.
  • Design and build robust ETL (Extract, Transform, Load) and ELT workflows to ingest, transform, and load structured and unstructured data from various sources.
  • Utilize Databricks features like Delta Live Tables and Databricks Workflows to orchestrate and manage complex data processes.
  • Optimize and tune Apache Spark jobs for performance and cost efficiency on large datasets.
  • Implement and enforce data security, access control, and compliance policies in Databricks and Azure.
  • Hands on experience working on streaming technologies (Kafka, Event Hubs, Kinesis).
  • Experience architecting machine learning platforms, advanced analytics workloads and develop and deployment of MLOPS on Databricks.
  • Expertise with enterprise security models, networking, and cost governance.
  • Should have experience working with DevOps teams to establish deployment practices using Terraform or similar.
  • Optimize Databricks performance (auto-scaling, caching, delta optimization, job tuning, cost optimization).
  • Prior experience in leading enterprise Azure Databricks implementations

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