Long applicationsign-up on Oracle

Sr. Databricks Engineer

EXL • Business Process Outsourcing (BPO) • AI Consulting & Integration • Bachelor's Degree • Python • SQL • Power BI • ETL/ELT • Master Data Management • 8 days ago
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Salary
≈ $22k – $46k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior · 9+ years exp

Confirmed on the employer's own hiring board on Oct 23, 2026. First seen by Alion on Oct 15, 2026. EXL scores B on the Alion truth index.

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Impact
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EXL

EXL, legally ExlService Holdings, is a data analytics, AI and digital operations company headquartered in New York that runs outsourced business processes and builds data and AI solutions for insurers, healthcare organisations, banks, media and retail companies. Founded in 1999 and listed on Nasdaq, it has more than 60,000 employees across six continents, with large delivery centers in Noida, Gurgaon, Pune, Bengaluru and Chennai and a newer AI hub in Dublin. It hires data scientists and data engineers, GenAI and MLOps engineers, analytics managers, solution consultants and client partners, plus talent acquisition, finance and medical coding staff.
exlservice.com • HQ: New York, United States • AI Consulting & Integration • Analytics & BI Consulting • Business Process Outsourcing (BPO) • 5000+ employees • Est. 1999
Tech stack of EXL

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 withminimum 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 withminimum 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 withminimum 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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