Senior Databricks Consultant / Architect

Role Overview

We are looking for an experienced Senior Databricks Consultant / Architect to lead the design, architecture, development, and implementation of enterprise-scale data engineering and analytics solutions using Azure Databricks. The ideal candidate should have strong expertise in Lakehouse architecture, PySpark, Delta Lake, cloud data platforms, and data governance.

Key Responsibilities

• Design and architect scalable, secure, and high-performance Lakehouse solutions using Databricks.
• Lead end-to-end data engineering and data platform implementation.
• Define enterprise data architecture, standards, best practices, and technical roadmaps.
• Design and implement data pipelines using PySpark, Spark SQL, Delta Lake, and Databricks Workflows.
• Develop and optimize ETL/ELT pipelines for batch and streaming data processing.
• Implement Medallion Architecture (Bronze, Silver, Gold) and Delta Live Tables (DLT).
• Work extensively with Unity Catalog for data governance, security, access control, and lineage.
• Integrate Databricks with Azure services such as ADLS Gen2, Azure Data Factory, Event Hubs, and Azure Key Vault.
• Optimize Spark workloads, cluster configurations, SQL queries, and overall platform performance.
• Implement CI/CD pipelines, automated deployments, and DevOps practices.
• Provide technical leadership, conduct design reviews, and mentor data engineering teams.
• Collaborate with business stakeholders, architects, and engineering teams to translate business requirements into technical solutions.

Mandatory Technical Skills

• Strong hands-on experience with Azure Databricks and Apache Spark.
• Expertise in PySpark, Spark SQL, Python, and SQL.
• Strong knowledge of Delta Lake and Lakehouse architecture.
• Experience implementing Medallion Architecture.
• Expertise in Unity Catalog, Delta Live Tables (DLT), and Auto Loader.
• Strong understanding of ETL/ELT design and data pipeline development.
• Experience with Azure Data Lake Storage (ADLS Gen2) and Azure Data Factory.
• Knowledge of Databricks Workflows, cluster management, and performance tuning.
• Experience with data security, governance, and access management.
• Hands-on experience with Git, Azure DevOps, and CI/CD.

Good to Have

• Experience with AWS Databricks or GCP Databricks.
• Knowledge of Kafka, Event Hubs, and real-time data streaming.
• Experience with Databricks SQL and Lakehouse Monitoring.
• Knowledge of Terraform and Infrastructure as Code.
• Experience with MLflow and the Databricks Machine Learning ecosystem.
• Databricks Professional-level certifications.

Candidate Profile

• Strong experience in enterprise data architecture and large-scale data engineering implementations.
• Ability to independently drive architecture decisions and technical delivery.
• Excellent communication, stakeholder management, and problem-solving skills.
• Ability to work with cross-functional teams and provide technical direction.