Role & responsibilities We are looking for a highly experienced Senior Cloud Data Engineer to lead the design, development, and optimization of our cloud-based data infrastructure. This role requires deep technical expertise in AWS services, data engineering best practices, and infrastructure automation. You will be instrumental in shaping our data architecture and enabling data-driven decision-making across the organization. Key Responsibilities: Design, build, and maintain scalable and secure data pipelines using AWS Glue , Redshift , and Python . Develop and optimize SQL queries and stored procedures for complex data transformations and migrations. Automate infrastructure provisioning and deployment using Terraform , ensuring repeatability and compliance. Architect and implement data lake and data warehouse solutions on AWS. Collaborate with cross-functional teams including data scientists, analysts, and DevOps to deliver high-quality data solutions. Monitor, troubleshoot, and optimize data workflows for performance, reliability, and cost-efficiency. Implement data quality checks, validation frameworks, and monitoring tools. Ensure data security, privacy, and compliance with industry standards and regulations. Lead code reviews, mentor junior engineers, and promote best practices in data engineering. Participate in capacity planning, cost optimization, and performance tuning of cloud data infrastructure. Evaluate and integrate new tools and technologies to improve data engineering capabilities. Document technical designs, processes, and operational procedures. Support business intelligence and analytics teams by ensuring timely and accurate data availability. Required Skills & Experience: 10+ years of experience in data engineering or cloud data architecture. Strong expertise in AWS Redshift , including schema design, performance tuning, and workload management. Proficiency in SQL and stored procedures for ETL and data migration tasks. Hands-on experience with Terraform for infrastructure as code (IaC) in AWS environments. Deep knowledge of AWS Glue for ETL orchestration and job development. Advanced programming skills in Python , especially for data processing and automation. Solid understanding of data warehousing, data lakes, and cloud-native data architectures. Preferred candidate profile AWS Certifications (e.g., AWS Certified Data Analytics Specialty, AWS Certified Solutions Architect). Experience with CI/CD pipelines and DevOps practices. Familiarity with additional AWS services like S3, Lambda, CloudWatch, Step Functions, and IAM. Knowledge of data governance, lineage, and cataloging tools (e.g., AWS Glue Data Catalog, Apache Atlas). Experience with real-time data processing frameworks (e.g., Kinesis, Kafka, Spark Streaming).