Blend360 Senior Data Engineer - Azure Databricks \\\| SmartRecruiters Google Chrome Microsoft Edge Apple Safari Mozilla Firefox . Senior Data Engineer - Azure Databricks Full-time Company Description Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com Job Description We are looking for an experienced Senior Azure Databricks Engineer with strong hands-on expertise in Python, SQL, and Apache Spark to design, build, and optimize scalable data pipelines and analytics solutions on the Azure cloud platform. The ideal candidate should have experience working with large datasets, distributed data processing, analytics use cases, and modern data engineering practices. Responsibilities Design, develop, and maintain scalable data pipelines using Azure Databricks Implement ETL/ELT workflows using PySpark, Spark SQL, and Python Implement pipelines for data ingestion using Azure Data Factory Optimize Spark jobs for performance, cost, and scalability Work with structured and semi-structured data (Parquet, Delta, JSON, CSV) Build and manage Delta Lake tables (ACID, time travel, schema evolution) Integrate Databricks with Azure Data Lake Storage (ADLS Gen2) Develop complex queries and transformations using SQL Collaborate with Data Science teams to prepare data for modelling use cases,ensuring appropriate transformations, feature generation, and storage. Follow best practices for security, access control, and governance in Azure Ensure data quality, validation, and monitoring using testing tools Deployment of solutions to Production environments Qualifications 4+ years of experience in Data Engineering, ideally supporting POS and SKU datasets. Handling high volume transactional datasets Strong hands-on experience with Azure Databricks Understanding of the Medallion Architecture and implementing it within Databricks Good understanding of data modelling techniques Proficiency in Python for data processing Strong knowledge of SQL (joins, window functions, performance tuning) Hands-on experience with Apache Spark / PySpark Experience working with Delta Lake Knowledge of Azure Data Lake Storage (ADLS Gen2) Understanding of distributed computing concepts Experience with Git version control Understanding of ML use cases and data considerations for model development I'm interested I'm interestedPrivacy Notice ImprintCookies Settings I'm interested Refer a friend share this job Share on LinkedIn Share on Facebook Share on Twitter Share via email Share on Xing Share on WeChat Share to WeChat × Copy the link and open WeChat to share. Copy to clipboard Open WeChat Share to WeChat × Use Scan QR Code in WeChat and click ··· to share. Senior Data Engineer - Azure Databricks Hyderabad, India Full-time I'm interested I'm interested

The role requires over four years of experience in Data Engineering, with a preference for supporting POS and SKU datasets and handling high-volume transactional data. Strong hands-on expertise in Azure Databricks is essential, along with a good understanding of data modeling techniques. Proficiency in Python for data processing and strong knowledge of SQL, including joins, window functions, and performance tuning, are necessary. Hands-on experience with Apache Spark/PySpark and Delta Lake is required, as is knowledge of Azure Data Lake Storage (ADLS Gen2). An understanding of distributed computing concepts and experience with Git version control are also needed. Familiarity with ML use cases and data considerations for model development is beneficial.