{"id":119785,"url":"https://alion.io/job/brillio-databricks-data-specialist-r01569707","title":"Databricks Data Specialist - R01569707","company":{"id":3063,"name":"Brillio","domain":"brillio.com","url":"https://alion.io/company/brillio","size_band":"501-1000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":76,"open_postings":44,"ghost_share":0,"stale_share":0.955,"repost_share":0,"time_to_fill_p50_days":29,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"hybrid","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":15000,"max_usd":41000,"period":"year","method":"role_country_seniority_unknown","sample_n":171},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"pySpark","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Airflow","optional":true},{"name":"Alation","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AWS","optional":true},{"name":"AWS Glue","optional":true},{"name":"AWS Lambda","optional":true},{"name":"AWS Step Functions","optional":true},{"name":"Azure","optional":true},{"name":"Azure Data Factory","optional":true},{"name":"Collibra","optional":true},{"name":"Fivetran","optional":true},{"name":"GCP","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Informatica","optional":true},{"name":"Microsoft Fabric","optional":true},{"name":"Power BI","optional":true},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-08-10T06:19:50Z","employer_posted_date":"2026-08-10","last_verified_at":"2026-10-26T10:39:58Z","board_verified":true,"closed_at":null,"days_open":77,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":77},"description":"Data Specialist\nPrimary Skills\nDatabricks Engineer\nRole Overview\nWe are seeking a highly skilled Databricks Engineer to design, develop, and optimize scalable data engineering and analytics solutions on the Databricks Lakehouse Platform. The ideal candidate will possess strong expertise in Databricks, PySpark, and SQL, with hands-on experience building batch and real-time data pipelines, implementing Lakehouse architectures, and ensuring data governance and performance optimization.\nKey Responsibilities\nDesign, develop, and maintain end-to-end data pipelines using Databricks and PySpark.\nBuild and implement Lakehouse architectures utilizing Bronze, Silver, and Gold data layers.\nDevelop and manage Delta Lake solutions with ACID transactions, schema enforcement, and data reliability features.\nCreate, monitor, and optimize Delta Live Tables (DLT) pipelines.\nImplement scalable and efficient data ingestion processes using Auto Loader.\nDevelop and manage real-time data processing solutions using Structured Streaming.\nOrchestrate, schedule, and monitor data workflows using Databricks Workflows.\nDesign and implement Lakehouse data models to support reporting, analytics, and business intelligence requirements.\nEstablish and enforce data governance, security, and access controls using Unity Catalog.\nOptimize Spark jobs, SQL queries, and overall platform performance to ensure efficiency and scalability.\nCollaborate with cross-functional teams, including data analysts, architects, and business stakeholders, to deliver high-quality data solutions.\nRequired Skills (Must Have)\nDatabricks Platform\nDelta Lake\nDelta Live Tables (DLT)\nUnity Catalog\nDatabricks Workflows\nPySpark and Apache Spark\nStructured Streaming\nAuto Loader\nSQL\nLakehouse Data Modeling\nStrong understanding of data engineering best practices and scalable data architectures\nPreferred Skills (Good to Have)\nAzure Ecosystem\nAzure Data Factory (ADF)\nAzure Synapse Analytics\nMicrosoft Purview\nMicrosoft Fabric\nAWS Ecosystem\nAWS Glue\nAWS Lambda\nAWS Step Functions\nData Engineering & Integration\nApache Airflow\nDBT\nFivetran\nInformatica\nStreaming & Analytics\nApache Kafka\nPower BI\nData Governance\nCollibra\nAlation\nGCP\nBigQuery\nQualifications\nBachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or a related discipline.\nProven experience in designing and implementing cloud-based data engineering solutions and scalable data pipelines.\nStrong analytical, troubleshooting, and problem-solving capabilities.\nExperience working in agile and collaborative environments.\nExcellent communication and stakeholder management skills.\nPreferred Candidate Profile\nHands-on experience with modern Lakehouse architectures and enterprise-scale data platforms.\nStrong understanding of data governance, security, and compliance frameworks.\nExperience delivering both batch and real-time data processing solutions.\nAbility to work independently while collaborating effectively across global teams.\nKey Technologies\nDatabricks | PySpark | Apache Spark | Delta Lake | Delta Live Tables (DLT) | Unity Catalog | Structured Streaming | Auto Loader | SQL | Lakehouse Architecture | Azure | AWS | Airflow | Kafka | Power BI\nSpecialization\nDatabricks Engineering: Lead Data Engineer\nJob requirements\nDatabricks Engineer\nRole Overview\nWe are seeking a highly skilled Databricks Engineer to design, develop, and optimize scalable data engineering and analytics solutions on the Databricks Lakehouse Platform. The ideal candidate will possess strong expertise in Databricks, PySpark, and SQL, with hands-on experience building batch and real-time data pipelines, implementing Lakehouse architectures, and ensuring data governance and performance optimization.\nKey Responsibilities\nDesign, develop, and maintain end-to-end data pipelines using Databricks and PySpark.\nBuild and implement Lakehouse architectures utilizing Bronze, Silver, and Gold data layers.\nDevelop and manage Delta Lake solutions with ACID transactions, schema enforcement, and data reliability features.\nCreate, monitor, and optimize Delta Live Tables (DLT) pipelines.\nImplement scalable and efficient data ingestion processes using Auto Loader.\nDevelop and manage real-time data processing solutions using Structured Streaming.\nOrchestrate, schedule, and monitor data workflows using Databricks Workflows.\nDesign and implement Lakehouse data models to support reporting, analytics, and business intelligence requirements.\nEstablish and enforce data governance, security, and access controls using Unity Catalog.\nOptimize Spark jobs, SQL queries, and overall platform performance to ensure efficiency and scalability.\nCollaborate with cross-functional teams, including data analysts, architects, and business stakeholders, to deliver high-quality data solutions.\nRequired Skills (Must Have)\nDatabricks Platform\nDelta Lake\nDelta Live Tables (DLT)\nUnity Catalog\nDatabricks Workflows\nPySpark and Apache Spark\nStructured Streaming\nAuto Loader\nSQL\nLakehouse Data Modeling\nStrong understanding of data engineering best practices and scalable data architectures\nPreferred Skills (Good to Have)\nAzure Ecosystem\nAzure Data Factory (ADF)\nAzure Synapse Analytics\nMicrosoft Purview\nMicrosoft Fabric\nAWS Ecosystem\nAWS Glue\nAWS Lambda\nAWS Step Functions\nData Engineering & Integration\nApache Airflow\nDBT\nFivetran\nInformatica\nStreaming & Analytics\nApache Kafka\nPower BI\nData Governance\nCollibra\nAlation\nGCP\nBigQuery\nQualifications\nBachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or a related discipline.\nProven experience in designing and implementing cloud-based data engineering solutions and scalable data pipelines.\nStrong analytical, troubleshooting, and problem-solving capabilities.\nExperience working in agile and collaborative environments.\nExcellent communication and stakeholder management skills.\nPreferred Candidate Profile\nHands-on experience with modern Lakehouse architectures and enterprise-scale data platforms.\nStrong understanding of data governance, security, and compliance frameworks.\nExperience delivering both batch and real-time data processing solutions.\nAbility to work independently while collaborating effectively across global teams.\nKey Technologies\nDatabricks | PySpark | Apache Spark | Delta Lake | Delta Live Tables (DLT) | Unity Catalog | Structured Streaming | Auto Loader | SQL | Lakehouse Architecture | Azure | AWS | Airflow | Kafka | Power BI","description_format":"text","description_chars":6467,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Blockchain & Crypto","AI Consulting & Integration","Cloud Consulting & Migration","Data Engineering & Migration Services"],"lifecycle":[{"event":"open","at":"2026-08-10T06:19:50Z"}],"visa":[],"liveness":{"score":7,"band":"cold","label":"Long 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