Senior AWS Data Engineer

Job Title: Senior AWS Data Engineer Experience: 10+ Years Employment Type: Full-Time Industry: Technology / Cloud / Financial Services / Healthcare / Retail About the Role We are seeking a Senior AWS Data Engineer with strong hands-on experience in building scalable, secure, and high-performance data pipelines on AWS. The ideal candidate will have a solid foundation in ETL frameworks, cloud platforms, and big data tools, with the ability to drive end-to-end data engineering solutions. Responsibilities • Design, develop, and maintain scalable ETL/ELT data pipelines using AWS-native and open-source tools. • Ingest, clean, and transform large volumes of structured and unstructured data. • Work with data lakes, data warehouses, and real-time data processing frameworks. • Develop and manage data models and implement best practices for data partitioning, compression, and optimization. • Collaborate with data scientists, analysts, and business stakeholders to deliver curated, high-quality datasets. • Implement and manage data quality checks, auditing, and monitoring processes. • Build and automate data orchestration workflows using tools like Apache Airflow or Step Functions. • Ensure data platforms are secure, compliant, and cost-optimized using services like IAM, KMS, S3, and Redshift. • Participate in code reviews, documentation, and mentoring junior engineers. Key Technologies & Tools • Cloud Platforms: AWS (S3, Glue, Redshift, Lambda, IAM, Athena, EMR, CloudFormation) • Programming: Python, SQL, Bash • ETL / Data Processing: Apache Airflow, Spark, Pandas • Databases: Snowflake, PostgreSQL, MySQL • DevOps / CI-CD: Git, Docker, Terraform (optional) • Other Tools: Linux/Unix, Jupyter, Visual Studio Code, AWS CLI Qualifications • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. • 6+ years of experience in data engineering or software development roles. • At least 3 years of deep experience in AWS-based data pipelines. • Strong SQL and Python skills; experience with PySpark is a plus. • Solid understanding of data warehousing, normalization, and modeling techniques. • Experience with data versioning, schema evolution, and performance tuning. • Working knowledge of cloud cost optimization, security, and governance. Preferred Skills (Nice to Have) • Experience with Snowflake or modern data platforms. • Knowledge of Kafka, Kinesis, or event-driven architectures. • Exposure to machine learning pipelines or data science workflows. Demonstrated ability to collaborate effectively within cross-functional teams to deliver scalable and reliable data solutions. Proven track record of leading initiatives to optimize data processing workflows and improve overall system efficiency.