AWS Data Engineer (Redshift | PySpark | SQL)

About Durapid TechnologiesDurapid Technologies is a Microsoft Data & AI Solutions Partner and Databricks partner delivering Data Engineering, AI/ML, and Enterprise Software solutions to clients across India, the USA, the UK, the Middle East, and Singapore.About The RoleWe’re looking for an AWS Data Engineer with 5+ years of experience strong hands-on expertise in PySpark, SQL, and AWS Redshift to design, build, and optimize scalable data pipelines and cloud data platforms. You’ll work closely with data architects, data scientists, analysts, and business stakeholders to deliver reliable, high-performance data solutions for analytics, reporting, and AI/ML initiatives.Key ResponsibilitiesDesign, develop, and maintain scalable data pipelines using PySpark and AWS servicesBuild and optimize ETL/ELT workflows for structured and unstructured dataDevelop and manage enterprise data warehouses on AWS RedshiftWrite complex, optimized SQL for extraction, transformation, and analysisIngest data from APIs, databases, flat files, and streaming sourcesPerform data cleansing, validation, and quality checksTune Redshift schemas, tables, and queries for performance and scaleApply data governance, security, and compliance best practicesMonitor, troubleshoot, and resolve pipeline and performance issuesSupport production deployments and maintain technical documentationMust-Have SkillsAWS Redshift (hands-on, mandatory)Strong PySpark and advanced SQL with performance optimizationAWS S3 and AWS GlueETL/ELT pipeline development, data modeling, and data warehousingWillingness to work from the Chennai office 5 days a weekGood-to-Have SkillsAWS Lambda, IAM, CloudWatch, Step Functions, Secrets ManagerPostgreSQL, MySQL; SQL ServerGit/GitHub/Bitbucket, CI/CD, Shell and Python scriptingData Lake / Lakehouse architecture; Databricks or Amazon EMRApache Airflow / MWAA, Kafka or real-time streamingTerraform or CloudFormationSnowflake or other cloud data warehousesExperience supporting BI, analytics, or ML workloadsQualificationsBachelor’s or Master’s in Computer Science, Engineering, IT, or a related field5+ years in Data Engineering, preferably on AWSProven track record building large-scale pipelines and warehouse solutionsStrong problem-solving, communication, and stakeholder management skillsSkills: data,data engineering,aws