Senior Technical Lead

Job description

Design and develop ETL/ELT solutions on Azure Databricks, LakeBase and Spark Develop, implement, and deploy large scale data pipelines empowering machine learning algorithms, insights generation, business intelligence dashboards, reporting and new data products Design, build, optimize, and manage modern large-scale data pipelines ETL/ELT processing to support data integration for analytics, machine learning features and predictive modelling Consume data from a variety of sources (RDBMS, APIs, FTPs and other cloud storage) & formats (Excel, CSV, XML, JSON, Parquet, Unstructured) Write advanced / complex SQL with performance tuning and optimization Identify ways to improve data reliability, data integrity, system efficiency and quality Participate in architectural evolution of data engineering patterns, frameworks, systems, and platforms including defining best practices and standards for managing data collections and integration Mentor other data engineers and provide technical direction by teaching other data engineers how to leverage cloud data platforms Required Qualifications: 7 + years of experience in data engineering, data integration, data modeling, data architecture, and ETL/ELT processes to provide quality data and analytics solutions 5 + years of experience in Python 2 + years of experience in Apache Spark (PySpark/Spark SQL) 2+ years building and deploying Cloud based solutions using - Azure Databricks with UC, Snowflake, Functions, Service Bus 2+ years of experience in SQL with designing complex data schemas and query performance optimization Experience with DevOps automation with Terraform. Experience with CI/CD process and tools – GitHub Actions, GIT, Artifactory, Sonar Preferred Qualifications: Bachelor’s degree in Computer Science, Engineering, Mathematics or related discipline Extensive knowledge of data architecture principles (e.g., Data Lake, Databricks Delta Lake, Data Warehousing, etc.) Extensive knowledge of data modelling techniques including slowly changing dimensions, aggregation, partitioning and indexing strategies Experience working with LLMs Ability to independently troubleshoot and performance tune large scale enterprise systems Excellent collaborator with experience working effectively with cross-functional teams such as leadership, product management and engineering, with a willingness to inspire other data engineers, data scientists and analysts Solid communication skills with the ability to communicate technical concepts to both technical and non-technical audiences Key Responsibilities Design and develop ETL/ELT solutions on Azure Databricks, LakeBase and Spark Develop, implement, and deploy large scale data pipelines empowering machine learning algorithms, insights generation, business intelligence dashboards, reporting and new data products Design, build, optimize, and manage modern large-scale data pipelines ETL/ELT processing to support data integration for analytics, machine learning features and predictive modelling Consume data from a variety of sources (RDBMS, APIs, FTPs and other cloud storage) & formats (Excel, CSV, XML, JSON, Parquet, Unstructured) Write advanced / complex SQL with performance tuning and optimization Identify ways to improve data reliability, data integrity, system efficiency and quality Participate in architectural evolution of data engineering patterns, frameworks, systems, and platforms including defining best practices and standards for managing data collections and integration Mentor other data engineers and provide technical direction by teaching other data engineers how to leverage cloud data platforms Required Qualifications: 7 + years of experience in data engineering, data integration, data modeling, data architecture, and ETL/ELT processes to provide quality data and analytics solutions 5 + years of experience in Python 2 + years of experience in Apache Spark (PySpark/Spark SQL) 2+ years building and deploying Cloud based solutions using - Azure Databricks with UC, Snowflake, Functions, Service Bus 2+ years of experience in SQL with designing complex data schemas and query performance optimization Experience with DevOps automation with Terraform. Experience with CI/CD process and tools – GitHub Actions, GIT, Artifactory, Sonar Preferred Qualifications: Bachelor’s degree in Computer Science, Engineering, Mathematics or related discipline Extensive knowledge of data architecture principles (e.g., Data Lake, Databricks Delta Lake, Data Warehousing, etc.) Extensive knowledge of data modelling techniques including slowly changing dimensions, aggregation, partitioning and indexing strategies Experience working with LLMs Ability to independently troubleshoot and performance tune large scale enterprise systems Excellent collaborator with experience working effectively with cross-functional teams such as leadership, product management and engineering, with a willingness to inspire other data engineers, data scientists and analysts Solid communication skills with the ability to communicate technical concepts to both technical and non-technical audiences Skill Requirements Other Requirements Apply now Information at a Glance Why HCLTech? At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first. HCLTech is a global technology company, home to more than 230,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending September 2026 totaled $14.8 billion. • Disclaimer • Privacy Statement • Terms of use • Sitemap • Raise a Grievance • We use cookies on our site. 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