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Azure Databricks Architect

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Onsite

Azure Databricks Architect

Tredence

Azure Databricks Architect

Skills

Google Cloud PlatformSQL QueriesData EngineeringPythonData ScienceDatabricksData AnalysisMachine LearningAIScalaAWSApache AirflowTerraformAzure

1 month ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

10 September 2026

What they need


Role: Azure Databricks Architect

Exp Level: 8-10 Years

Work Location: Bangalore

We are seeking a Databricks Architect to design and lead our enterprise data and AI platform built on Databricks. You will define the architecture for large-scale data engineering, analytics, and machine learning workloads on the Lakehouse platform.

Key Responsibilities

  • Design end-to-end Lakehouse architectures on Databricks, including ingestion, transformation, storage, and consumption layers.
  • Define best practices for Delta Lake, Unity Catalog, and workspace governance across environments.
  • Architect scalable data pipelines using Spark, Delta Live Tables, and Databricks Workflows.
  • Establish security, access control, and data governance standards within Databricks and the underlying cloud platform (Azure/AWS/GCP).
  • Guide performance tuning and cost optimization of Spark jobs and cluster configurations.
  • Collaborate with data engineering, data science, and ML teams to enable a unified data and AI platform.
  • Define CI/CD and DevOps practices for Databricks notebooks, jobs, and infrastructure (Repos, Terraform).
  • Provide architectural oversight and mentorship to data engineering teams.
  • Partner with stakeholders to align the Databricks platform roadmap with business and analytics needs.

Required Skills & Qualifications

  • 8-10 years of overall data engineering/architecture experience, with at least 4.5 years hands-on with Databricks.
  • Deep expertise in Apache Spark, Delta Lake, and the Databricks Lakehouse architecture.
  • Strong experience with Unity Catalog, workspace administration, and cluster/job management.
  • Hands-on experience with at least one hyperscaler (Azure, AWS, or GCP) underlying the Databricks deployment.
  • Proficiency in Python and/or Scala, and strong SQL skills.
  • Experience designing batch and streaming data pipelines at enterprise scale.
  • Solid understanding of data governance, lineage, and security practices in a Lakehouse environment.
  • Strong communication skills and experience leading technical teams.

Preferred Qualifications (Good to Have)

  • Databricks Certified Data Engineer Professional or Databricks Certified Solutions Architect.
  • Experience with MLflow and enabling ML/AI workloads on Databricks.
  • Exposure to orchestration tools such as Airflow or Azure Data Factory.
  • Experience migrating legacy data warehouses to a Lakehouse architecture.

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