{"id":2859532,"url":"https://alion.io/job/exl-sr-databricks-engineer","title":"Sr. Databricks Engineer","company":{"id":38016,"name":"EXL","domain":"exlservice.com","url":"https://alion.io/company/exl","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":80,"open_postings":232,"ghost_share":0,"stale_share":0.991,"repost_share":0,"time_to_fill_p50_days":9,"computed_at":"2026-10-23T05:54:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":71},"experience_years_min":9,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Agile","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Master Data Management","optional":true},{"name":"Microsoft Fabric","optional":true},{"name":"Power BI","optional":true},{"name":"Scrum","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-10-15T13:01:47Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-23T20:51:38Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"Job Description: Azure / Databricks Data Engineer (9-15 Years Experience) Job Title\nSenior Azure Databricks Data Engineer\nExperience\n9-15 Years of IT Experience\nLocation\nHybrid\nJob Summary\nWe 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.\nKey Responsibilities\nDesign, develop, and maintain data pipelines using Databricks, Apache Spark, and cloud-native services.\nBuild and optimize ETL/ELT workflows for large-scale structured and unstructured data.\nDevelop data models and implement data quality, validation, and governance frameworks.\nIntegrate data from multiple sources into a unified Lakehouse architecture.\nOptimize Spark jobs and Databricks workloads for performance, scalability, and cost efficiency.\nImplement security controls, access management, and data governance using Unity Catalog.\nCollaborate with business, analytics, and AI/ML teams to deliver trusted data products.\nMonitor, troubleshoot, and resolve data pipeline issues.\nSupport CI/CD, DevOps, and infrastructure automation practices.\nMaintain technical documentation and best practices.\nRequired Technical Skills Core Technologies\nDatabricks Lakehouse Platform\nApache Spark / PySpark\nDelta Lake\nSQL\nPython\nData Engineering\nETL / ELT Development\nData Modeling\nData Warehousing\nData Quality & Validation\nStreaming & Real-Time Processing\nGovernance & Security\nUnity Catalog\nData Lineage\nRow-Level Security\nAccess Control & Compliance\nData Governance Frameworks\nCloud & DevOps\nAzure / AWS / GCP\nTerraform\nGitHub Actions / Azure DevOps\nCI/CD Pipelines\nAnalytics & AI\nSemantic Layers\nData Products\nBI Platforms\nMachine Learning Support\nGenerative AI & RAG Architectures\nRequired Qualifications\nBachelor's or Master's degree in Computer Science, Information Technology, Engineering, or related field.\n9-15 years of experience in Data Engineering and Data Warehousing.\nMinimum 5+ years of hands-on experience with Azure Data Engineering technologies.\nMinimum 4+ years of hands-on experience with Azure Databricks and Spark ecosystem.\nStrong understanding of data lake, lakehouse, and cloud-native architecture patterns.\nExperience in handling large-scale structured and unstructured datasets.\nStrong analytical, problem-solving, and troubleshooting skills.\nPreferred Qualifications\nMicrosoft Certified: Azure Data Engineer Associate (DP-203).\nDatabricks Certified Data Engineer Associate/Professional.\nExperience with Snowflake, Power BI, or Microsoft Fabric.\nExperience in real-time streaming solutions using Kafka/Event Hubs.\nExposure to Data Governance and Master Data Management initiatives.\nSoft Skills\nStrong stakeholder management and communication skills.\nAbility to lead technical initiatives and drive architecture discussions.\nExperience working in Agile/Scrum environments.\nExcellent documentation and presentation skills.\nStrong mentoring and team leadership abilities.\nNice to Have\nMicrosoft Fabric\nPower BI\nAzure Event Hubs\nKafka\nMachine Learning data pipelines\nData Governance tools such as Purview\nJob Description: Azure / Databricks Data Engineer (9-15 Years Experience) Job Title\nSenior Azure Databricks Data Engineer\nExperience\n9-15 Years of IT Experience\nLocation\nHybrid\nJob Summary\nWe 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.\nKey Responsibilities\nDesign, develop, and maintain data pipelines using Databricks, Apache Spark, and cloud-native services.\nBuild and optimize ETL/ELT workflows for large-scale structured and unstructured data.\nDevelop data models and implement data quality, validation, and governance frameworks.\nIntegrate data from multiple sources into a unified Lakehouse architecture.\nOptimize Spark jobs and Databricks workloads for performance, scalability, and cost efficiency.\nImplement security controls, access management, and data governance using Unity Catalog.\nCollaborate with business, analytics, and AI/ML teams to deliver trusted data products.\nMonitor, troubleshoot, and resolve data pipeline issues.\nSupport CI/CD, DevOps, and infrastructure automation practices.\nMaintain technical documentation and best practices.\nRequired Technical Skills Core Technologies\nDatabricks Lakehouse Platform\nApache Spark / PySpark\nDelta Lake\nSQL\nPython\nData Engineering\nETL / ELT Development\nData Modeling\nData Warehousing\nData Quality & Validation\nStreaming & Real-Time Processing\nGovernance & Security\nUnity Catalog\nData Lineage\nRow-Level Security\nAccess Control & Compliance\nData Governance Frameworks\nCloud & DevOps\nAzure / AWS / GCP\nTerraform\nGitHub Actions / Azure DevOps\nCI/CD Pipelines\nAnalytics & AI\nSemantic Layers\nData Products\nBI Platforms\nMachine Learning Support\nGenerative AI & RAG Architectures\nRequired Qualifications\nBachelor's or Master's degree in Computer Science, Information Technology, Engineering, or related field.\n9-15 years of experience in Data Engineering and Data Warehousing.\nMinimum 5+ years of hands-on experience with Azure Data Engineering technologies.\nMinimum 4+ years of hands-on experience with Azure Databricks and Spark ecosystem.\nStrong understanding of data lake, lakehouse, and cloud-native architecture patterns.\nExperience in handling large-scale structured and unstructured datasets.\nStrong analytical, problem-solving, and troubleshooting skills.\nPreferred Qualifications\nMicrosoft Certified: Azure Data Engineer Associate (DP-203).\nDatabricks Certified Data Engineer Associate/Professional.\nExperience with Snowflake, Power BI, or Microsoft Fabric.\nExperience in real-time streaming solutions using Kafka/Event Hubs.\nExposure to Data Governance and Master Data Management initiatives.\nSoft Skills\nStrong stakeholder management and communication skills.\nAbility to lead technical initiatives and drive architecture discussions.\nExperience working in Agile/Scrum environments.\nExcellent documentation and presentation skills.\nStrong mentoring and team leadership abilities.\nNice to Have\nMicrosoft Fabric\nPower BI\nAzure Event Hubs\nKafka\nMachine Learning data pipelines\nData Governance tools such as Purview\nJob Description: Azure / Databricks Data Engineer (9-15 Years Experience) Job Title\nSenior Azure Databricks Data Engineer\nExperience\n9-15 Years of IT Experience\nLocation\nHybrid\nJob Summary\nWe 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.\nKey Responsibilities\nDesign, develop, and maintain data pipelines using Databricks, Apache Spark, and cloud-native services.\nBuild and optimize ETL/ELT workflows for large-scale structured and unstructured data.\nDevelop data models and implement data quality, validation, and governance frameworks.\nIntegrate data from multiple sources into a unified Lakehouse architecture.\nOptimize Spark jobs and Databricks workloads for performance, scalability, and cost efficiency.\nImplement security controls, access management, and data governance using Unity Catalog.\nCollaborate with business, analytics, and AI/ML teams to deliver trusted data products.\nMonitor, troubleshoot, and resolve data pipeline issues.\nSupport CI/CD, DevOps, and infrastructure automation practices.\nMaintain technical documentation and best practices.\nRequired Technical Skills Core Technologies\nDatabricks Lakehouse Platform\nApache Spark / PySpark\nDelta Lake\nSQL\nPython\nData Engineering\nETL / ELT Development\nData Modeling\nData Warehousing\nData Quality & Validation\nStreaming & Real-Time Processing\nGovernance & Security\nUnity Catalog\nData Lineage\nRow-Level Security\nAccess Control & Compliance\nData Governance Frameworks\nCloud & DevOps\nAzure / AWS / GCP\nTerraform\nGitHub Actions / Azure DevOps\nCI/CD Pipelines\nAnalytics & AI\nSemantic Layers\nData Products\nBI Platforms\nMachine Learning Support\nGenerative AI & RAG Architectures\nRequired Qualifications\nBachelor's or Master's degree in Computer Science, Information Technology, Engineering, or related field.\n9-15 years of experience in Data Engineering and Data Warehousing.\nMinimum 5+ years of hands-on experience with Azure Data Engineering technologies.\nMinimum 4+ years of hands-on experience with Azure Databricks and Spark ecosystem.\nStrong understanding of data lake, lakehouse, and cloud-native architecture patterns.\nExperience in handling large-scale structured and unstructured datasets.\nStrong analytical, problem-solving, and troubleshooting skills.\nPreferred Qualifications\nMicrosoft Certified: Azure Data Engineer Associate (DP-203).\nDatabricks Certified Data Engineer Associate/Professional.\nExperience with Snowflake, Power BI, or Microsoft Fabric.\nExperience in real-time streaming solutions using Kafka/Event Hubs.\nExposure to Data Governance and Master Data Management initiatives.\nSoft Skills\nStrong stakeholder management and communication skills.\nAbility to lead technical initiatives and drive architecture discussions.\nExperience working in Agile/Scrum environments.\nExcellent documentation and presentation skills.\nStrong mentoring and team leadership abilities.\nNice to Have\nMicrosoft Fabric\nPower BI\nAzure Event Hubs\nKafka\nMachine Learning data pipelines\nData Governance tools 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