Tredence
Azure Databricks Architect
Skills
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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