Data Pipeline & Architecture

• Design, develop, and maintain robust ETL/ELT pipelines using Azure Databricks and Azure Data Factory (ADF).
• Architect and implement scalable data lakehouse solutions on Azure using Delta Lake.
• Build and optimize data workflows across batch and streaming workloads (Spark Structured Streaming, Event Hubs).
• Define and enforce data modeling best practices (star schema, data vault, medallion architecture).

Databricks Engineering

• Develop and optimize Spark-based data transformations using PySpark and Spark SQL in Databricks.
• Manage Databricks clusters, jobs, and workspace configurations for performance and cost efficiency.
• Implement Delta Live Tables (DLT) pipelines for declarative, auto-scaling data transformations.
• Leverage Unity Catalog for data governance, lineage tracking, and access control.
• Utilize Databricks Asset Bundles (DABs) and CI/CD practices for deployment automation.

Azure Data Factory (ADF) – (Good To Have Feature)

• Build, schedule, and monitor complex ADF pipelines with parameterized templates.
• Integrate ADF with Azure Key Vault, Linked Services, and Integration Runtimes (SHIR/Azure IR).
• Implement incremental load patterns, watermarking, and change data capture (CDC) strategies.
• Troubleshoot pipeline failures and optimize ADF pipeline performance and cost.

Data Quality & Governance

• Implement data quality frameworks and validation checks across pipelines.
• Enforce data cataloging, lineage, and metadata management practices.
• Collaborate with data governance teams to ensure compliance with data policies and regulations (GDPR, HIPAA).

Collaboration & Leadership

• Mentor junior data engineers and conduct code reviews.
• Work closely with data scientists and ML engineers to productionize machine learning models.
• Partner with DevOps/Cloud teams on infrastructure-as-code (Terraform/Bicep) for data platform provisioning.
• Document architecture decisions, pipeline designs, and operational runbooks.

• 7+ years of hands-on experience with Azure Databricks (Spark SQL, Delta Lake, PySpark).
• Strong proficiency in SQL preferred and Python.
• Deep understanding of distributed computing principles and the Apache Spark ecosystem.
• Experience with Azure data services: ADLS Gen2, Azure Synapse, Azure SQL, Event Hubs / Kafka.
• Solid understanding of data warehousing concepts and dimensional modeling.
• Experience with version control (Git) and CI/CD tools (Azure DevOps, GitHub Actions).
• Familiarity with infrastructure-as-code tools (Terraform or ARM/Bicep).

• Databricks Certified Data Engineer Associate or Professional certification.
• Microsoft Certified: Azure Data Engineer Associate (DP-203).
• Experience with Delta Live Tables (DLT) and Databricks Workflows.
• Familiarity with Power BI or other BI/reporting tools.
• Experience with Scala or Java for Spark development.

• Strong analytical and problem-solving skills with attention to detail.
• Excellent communication skills — ability to translate complex technical concepts to non-technical stakeholders.
• Self-motivated and able to manage competing priorities in an agile environment.
• Collaborative team player with a growth mindset and eagerness to mentor others.
• Proactive in identifying performance bottlenecks and proposing architectural improvements.

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