Key Responsibilities:

- Design, build, and maintain scalable data pipelines and ETL/ELT workflows on AWS.

- Develop and optimize cloud-based data architectures to support analytics and business intelligence.

- Integrate data from multiple sources, ensuring data quality, consistency, and reliability.

- Build and manage data lakes and data warehouses using AWS services.

- Optimize data processing, storage, and performance for large-scale datasets.

- Implement data security, governance, and compliance best practices.

- Collaborate with cross-functional teams to understand business requirements and deliver data-driven solutions.

- Monitor, troubleshoot, and enhance the performance of data pipelines and cloud infrastructure.

- Participate in code reviews, testing, deployment, and CI/CD processes.

Required Skills:

- Strong experience in Data Engineering with 6-15 years of industry experience.

- Hands-on expertise with AWS services such as S3, Glue, Lambda, EMR, Redshift, Athena, RDS, EC2, CloudWatch, and IAM.

- Experience in designing and developing ETL/ELT pipelines.

- Proficiency in Python, SQL, and data processing frameworks.

- Experience with big data technologies such as Apache Spark, Hadoop, or Kafka.

- Knowledge of data modeling, data warehousing, and cloud-native architectures.

- Familiarity with CI/CD pipelines, Git, and infrastructure automation.

- Strong analytical, troubleshooting, and problem-solving skills.

Preferred Skills:

- AWS certifications such as AWS Certified Data Engineer - Associate or AWS Certified Solutions Architect.

- Experience with Snowflake, Databricks, or other modern cloud data platforms.

- Knowledge of orchestration tools such as Apache Airflow.

- Familiarity with Agile/Scrum methodologies and DevOps practices.

- Experience with data governance, security, and compliance standards.