AWS Data Engineer

Key ResponsibilitiesDesign, 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.RequirementsExperience: 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, LambdaAzure: Data Lake Storage, Azure SQL, CosmosDB, Data Factory, SynapseData 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.