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Sr. Databricks Engineer

EXLEngineering & Development
Hiring from
India
Work type
Hybrid
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Job Description: Azure / Databricks Data Engineer (9-15 Years Experience) Job Title

Senior Azure Databricks Data Engineer

Experience

9-15 Years of IT Experience

Location

Hybrid

Job Summary

We are seeking a skilled Databricks Engineer with minimum of 5+ years of hands-on in Databricks who can design, develop, and optimize scalable data platforms and analytics solutions using the Databricks Lakehouse Platform. The ideal candidate will have expertise in data engineering, ETL/ELT development, cloud technologies preferably Azure, and big data processing to support enterprise analytics and AI initiatives.

Key Responsibilities
  • Design, develop, and maintain data pipelines using Databricks, Apache Spark, and cloud-native services.
  • Build and optimize ETL/ELT workflows for large-scale structured and unstructured data.
  • Develop data models and implement data quality, validation, and governance frameworks.
  • Integrate data from multiple sources into a unified Lakehouse architecture.
  • Optimize Spark jobs and Databricks workloads for performance, scalability, and cost efficiency.
  • Implement security controls, access management, and data governance using Unity Catalog.
  • Collaborate with business, analytics, and AI/ML teams to deliver trusted data products.
  • Monitor, troubleshoot, and resolve data pipeline issues.
  • Support CI/CD, DevOps, and infrastructure automation practices.
  • Maintain technical documentation and best practices.
Required Technical Skills Core Technologies
  • Databricks Lakehouse Platform
  • Apache Spark / PySpark
  • Delta Lake
  • SQL
  • Python

Data Engineering

  • ETL / ELT Development
  • Data Modeling
  • Data Warehousing
  • Data Quality & Validation
  • Streaming & Real-Time Processing
Governance & Security
  • Unity Catalog
  • Data Lineage
  • Row-Level Security
  • Access Control & Compliance
  • Data Governance Frameworks
Cloud & DevOps
  • Azure / AWS / GCP
  • Terraform
  • GitHub Actions / Azure DevOps
  • CI/CD Pipelines
Analytics & AI
  • Semantic Layers
  • Data Products
  • BI Platforms
  • Machine Learning Support
  • Generative AI & RAG Architectures
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or related field.
  • 9-15 years of experience in Data Engineering and Data Warehousing.
  • Minimum 5+ years of hands-on experience with Azure Data Engineering technologies.
  • Minimum 4+ years of hands-on experience with Azure Databricks and Spark ecosystem.
  • Strong understanding of data lake, lakehouse, and cloud-native architecture patterns.
  • Experience in handling large-scale structured and unstructured datasets.
  • Strong analytical, problem-solving, and troubleshooting skills.

Preferred Qualifications
  • Microsoft Certified: Azure Data Engineer Associate (DP-203).
  • Databricks Certified Data Engineer Associate/Professional.
  • Experience with Snowflake, Power BI, or Microsoft Fabric.
  • Experience in real-time streaming solutions using Kafka/Event Hubs.
  • Exposure to Data Governance and Master Data Management initiatives.

Soft Skills
  • Strong stakeholder management and communication skills.
  • Ability to lead technical initiatives and drive architecture discussions.
  • Experience working in Agile/Scrum environments.
  • Excellent documentation and presentation skills.
  • Strong mentoring and team leadership abilities.

Nice to Have
  • Microsoft Fabric
  • Power BI
  • Azure Event Hubs
  • Kafka
  • Machine Learning data pipelines
  • Data Governance tools such as Purview

Job Description: Azure / Databricks Data Engineer (9-15 Years Experience) Job Title

Senior Azure Databricks Data Engineer

Experience

9-15 Years of IT Experience

Location

Hybrid

Job Summary

We are seeking a skilled Databricks Engineer with minimum of 5+ years of hands-on in Databricks who can design, develop, and optimize scalable data platforms and analytics solutions using the Databricks Lakehouse Platform. The ideal candidate will have expertise in data engineering, ETL/ELT development, cloud technologies preferably Azure, and big data processing to support enterprise analytics and AI initiatives.

Key Responsibilities
  • Design, develop, and maintain data pipelines using Databricks, Apache Spark, and cloud-native services.
  • Build and optimize ETL/ELT workflows for large-scale structured and unstructured data.
  • Develop data models and implement data quality, validation, and governance frameworks.
  • Integrate data from multiple sources into a unified Lakehouse architecture.
  • Optimize Spark jobs and Databricks workloads for performance, scalability, and cost efficiency.
  • Implement security controls, access management, and data governance using Unity Catalog.
  • Collaborate with business, analytics, and AI/ML teams to deliver trusted data products.
  • Monitor, troubleshoot, and resolve data pipeline issues.
  • Support CI/CD, DevOps, and infrastructure automation practices.
  • Maintain technical documentation and best practices.
Required Technical Skills Core Technologies
  • Databricks Lakehouse Platform
  • Apache Spark / PySpark
  • Delta Lake
  • SQL
  • Python

Data Engineering

  • ETL / ELT Development
  • Data Modeling
  • Data Warehousing
  • Data Quality & Validation
  • Streaming & Real-Time Processing
Governance & Security
  • Unity Catalog
  • Data Lineage
  • Row-Level Security
  • Access Control & Compliance
  • Data Governance Frameworks
Cloud & DevOps
  • Azure / AWS / GCP
  • Terraform
  • GitHub Actions / Azure DevOps
  • CI/CD Pipelines
Analytics & AI
  • Semantic Layers
  • Data Products
  • BI Platforms
  • Machine Learning Support
  • Generative AI & RAG Architectures
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or related field.
  • 9-15 years of experience in Data Engineering and Data Warehousing.
  • Minimum 5+ years of hands-on experience with Azure Data Engineering technologies.
  • Minimum 4+ years of hands-on experience with Azure Databricks and Spark ecosystem.
  • Strong understanding of data lake, lakehouse, and cloud-native architecture patterns.
  • Experience in handling large-scale structured and unstructured datasets.
  • Strong analytical, problem-solving, and troubleshooting skills.

Preferred Qualifications
  • Microsoft Certified: Azure Data Engineer Associate (DP-203).
  • Databricks Certified Data Engineer Associate/Professional.
  • Experience with Snowflake, Power BI, or Microsoft Fabric.
  • Experience in real-time streaming solutions using Kafka/Event Hubs.
  • Exposure to Data Governance and Master Data Management initiatives.

Soft Skills
  • Strong stakeholder management and communication skills.
  • Ability to lead technical initiatives and drive architecture discussions.
  • Experience working in Agile/Scrum environments.
  • Excellent documentation and presentation skills.
  • Strong mentoring and team leadership abilities.

Nice to Have
  • Microsoft Fabric
  • Power BI
  • Azure Event Hubs
  • Kafka
  • Machine Learning data pipelines
  • Data Governance tools such as Purview

Job Description: Azure / Databricks Data Engineer (9-15 Years Experience) Job Title

Senior Azure Databricks Data Engineer

Experience

9-15 Years of IT Experience

Location

Hybrid

Job Summary

We are seeking a skilled Databricks Engineer with minimum of 5+ years of hands-on in Databricks who can design, develop, and optimize scalable data platforms and analytics solutions using the Databricks Lakehouse Platform. The ideal candidate will have expertise in data engineering, ETL/ELT development, cloud technologies preferably Azure, and big data processing to support enterprise analytics and AI initiatives.

Key Responsibilities
  • Design, develop, and maintain data pipelines using Databricks, Apache Spark, and cloud-native services.
  • Build and optimize ETL/ELT workflows for large-scale structured and unstructured data.
  • Develop data models and implement data quality, validation, and governance frameworks.
  • Integrate data from multiple sources into a unified Lakehouse architecture.
  • Optimize Spark jobs and Databricks workloads for performance, scalability, and cost efficiency.
  • Implement security controls, access management, and data governance using Unity Catalog.
  • Collaborate with business, analytics, and AI/ML teams to deliver trusted data products.
  • Monitor, troubleshoot, and resolve data pipeline issues.
  • Support CI/CD, DevOps, and infrastructure automation practices.
  • Maintain technical documentation and best practices.
Required Technical Skills Core Technologies
  • Databricks Lakehouse Platform
  • Apache Spark / PySpark
  • Delta Lake
  • SQL
  • Python

Data Engineering

  • ETL / ELT Development
  • Data Modeling
  • Data Warehousing
  • Data Quality & Validation
  • Streaming & Real-Time Processing
Governance & Security
  • Unity Catalog
  • Data Lineage
  • Row-Level Security
  • Access Control & Compliance
  • Data Governance Frameworks
Cloud & DevOps
  • Azure / AWS / GCP
  • Terraform
  • GitHub Actions / Azure DevOps
  • CI/CD Pipelines
Analytics & AI
  • Semantic Layers
  • Data Products
  • BI Platforms
  • Machine Learning Support
  • Generative AI & RAG Architectures
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or related field.
  • 9-15 years of experience in Data Engineering and Data Warehousing.
  • Minimum 5+ years of hands-on experience with Azure Data Engineering technologies.
  • Minimum 4+ years of hands-on experience with Azure Databricks and Spark ecosystem.
  • Strong understanding of data lake, lakehouse, and cloud-native architecture patterns.
  • Experience in handling large-scale structured and unstructured datasets.
  • Strong analytical, problem-solving, and troubleshooting skills.

Preferred Qualifications
  • Microsoft Certified: Azure Data Engineer Associate (DP-203).
  • Databricks Certified Data Engineer Associate/Professional.
  • Experience with Snowflake, Power BI, or Microsoft Fabric.
  • Experience in real-time streaming solutions using Kafka/Event Hubs.
  • Exposure to Data Governance and Master Data Management initiatives.

Soft Skills
  • Strong stakeholder management and communication skills.
  • Ability to lead technical initiatives and drive architecture discussions.
  • Experience working in Agile/Scrum environments.
  • Excellent documentation and presentation skills.
  • Strong mentoring and team leadership abilities.

Nice to Have
  • Microsoft Fabric
  • Power BI
  • Azure Event Hubs
  • Kafka
  • Machine Learning data pipelines
  • Data Governance tools such as Purview

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