AWS Data Engineer
First seen by Alion on Oct 9, 2026.
About the Role :
Looking for an experienced Data Engineer with strong hands-on expertise in AWS cloud services, data ingestion, ETL pipelines, and data warehousing. The ideal candidate should have experience in building scalable data pipelines, managing data integration, and ensuring data quality and governance using modern data engineering technologies.
Key Responsibilities :
- Perform conceptual analysis, problem-solving, and data analysis to support business requirements.
- Take ownership of data engineering initiatives and projects, translating business requirements into effective data solutions.
- Collaborate with subject matter experts, business analysts, data analysts, and other stakeholders for data integration and alignment.
- Support enterprise data usage by identifying optimal data sources, data mapping strategies, and underlying business models.
- Develop and maintain data pipelines to ingest data from databases, delimited files, and other sources.
- Perform complex data transformations and load processed data into Amazon S3.
- Develop AWS Glue mappings and maintain the AWS Glue Data Catalog.
- Design and implement ETL pipelines and data warehouse solutions using Python, PySpark, and Amazon Redshift.
- Ensure data quality, reliability, cleanliness, and consistency across multiple data sources.
- Create standardized data deliverables in accordance with corporate data standards and definitions.
- Work with Change Data Capture (CDC) and event streaming technologies to support efficient data processing.
- Evaluate and support the adoption of modern data formats and technologies, including Parquet, Apache Iceberg, and unstructured data solutions.
Required Skills & Qualifications :
- 4 - 6 years of overall experience in data engineering, with at least 2 - 3 years of strong hands-on experience in data ingestion, ETL, and data warehousing.
- Strong hands-on experience with AWS cloud services for data management and governance.
- Proficiency in Python and PySpark for data processing and transformation.
- Hands-on experience with AWS Glue, Glue Data Catalog, Amazon S3, and Amazon Redshift.
- Experience designing and implementing scalable data pipelines using industry-standard practices.
- Strong understanding of ETL processes, data integration, and data warehouse technologies.
- Experience with Change Data Capture (CDC) and event streaming technologies.
- Familiarity with Parquet, Apache Iceberg, and unstructured data processing.
- Strong analytical, problem-solving, and data management skills.
- Excellent collaboration and communication skills to work effectively with cross-functional teams.
Skills
Data Engineering, AWS, Python, PySpark, ETL, Data Warehousing, Data Management, Data Services