Azure Databrick SME

BayRock LabsMilpitas, CA
4h

About The Position

We are seeking a highly skilled Data Platform Engineer (SME level) with deep hands-on expertise in Azure Databricks, data engineering, and platform engineering. The ideal candidate will have strong experience in Microsoft Azure, DevOps, and automation, along with working knowledge of AI/ML Ops platforms. This role is critical in designing, building, and maintaining scalable, secure, and high-performance enterprise data solutions.

Requirements

  • Expert-level hands-on experience with Azure Databricks (clusters, notebooks, Delta Lake, MLflow).
  • Strong background in data engineering (ETL, data modeling, big data frameworks).
  • Proven expertise in platform engineering and enterprise-scale data solutions.
  • Proficiency in Microsoft Azure services (Data Lake, Synapse, Key Vault, Functions, Logic Apps).
  • Solid understanding of DevOps practices (CI/CD pipelines, Git, Terraform/ARM templates, automation).
  • Working knowledge of AI/ML Ops platforms (model deployment, monitoring, ML lifecycle management).
  • Strong skills in SQL, Python, and Spark for data processing and analytics.
  • Excellent problem-solving, communication, and stakeholder management skills.
  • 10+ years of experience in enterprise data engineering/platform roles preferred.

Nice To Haves

  • Experience with enterprise supply chain or manufacturing data platforms.
  • Familiarity with data governance frameworks and compliance standards.
  • Certifications in Azure Data Engineer Associate or Azure Solutions Architect Expert.

Responsibilities

  • Build and maintain enterprise-grade data platforms leveraging Azure Databricks.
  • Design, develop, and optimize data pipelines, ETL workflows, and orchestration processes.
  • Support platform engineering initiatives to enhance scalability, reliability, and performance.
  • Integrate AI/ML Ops capabilities into data platforms to enable advanced analytics and machine learning.
  • Implement DevOps and automation best practices for CI/CD, monitoring, and infrastructure as code.
  • Ensure security, compliance, and governance across all data systems.
  • Collaborate with cross-functional teams (data scientists, architects, business stakeholders) to deliver end-to-end solutions.
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