AWS Data Engineer

Key Responsibilities

• Design, build, and maintain scalable data pipelines, ETL/ELT workflows, and data integration processes.
• Develop and optimize SQL queries, data models, and data structures for analytics and operational use cases.
• Work with diverse data storage systems, including relational, NoSQL, and distributed file systems.
• Implement data processing solutions using distributed computing frameworks such as Apache Spark or Hadoop.
• Collaborate with cross‑functional teams to deliver cloud‑native data engineering solutions on AWS or Azure.
• Develop automated workflows using orchestration tools such as Apache Airflow or Azure Data Factory.
• Apply DevOps practices including CI/CD automation, IaC, and containerization.
• Contribute to the design of resilient, scalable, and secure data architecture.

Requirements

• Experience: Minimum 3 years of professional experience in data engineering, including at least 2 years working with cloud‑native data services.
• Programming: Proficiency in at least one programming language such as Python, Java, or .NET.
• Data Fundamentals:
• Strong SQL expertise in relational databases and data warehouses.
• Solid understanding of data modeling, data structures, and access patterns.
• Hands‑on experience with relational databases (e.g., PostgreSQL, MySQL), NoSQL systems (e.g., DynamoDB, CosmosDB), and distributed storage.
• Cloud Platforms: Practical experience with major cloud services (AWS or Azure), such as:
• AWS: S3, RDS/Aurora, EMR, Glue, Athena, Redshift, Lambda
• Azure: Data Lake Storage, Azure SQL, CosmosDB, Data Factory, Synapse
• Data Processing: Experience using frameworks like PySpark, Apache Spark, Hadoop, and libraries such as Pandas.
• Orchestration: Familiarity with Airflow, Azure Data Factory, or similar tools.
• DevOps & Governance:
• Experience with CI/CD pipeline development.
• Knowledge of IaC tools such as Git, Docker, and Terraform.
• Understanding of system design principles for data platforms.