Data Engineer
First seen by Alion on Sep 19, 2026.
About the Role :
We're seeking a Data Engineer with 3 - 4 years of experience to join our growing tech team. The ideal candidate will have hands-on experience in building and managing scalable data systems on AWS using Spark and modern data frameworks.
Key Responsibilities :
- Design, build, and maintain end-to-end data pipelines for ingestion, transformation, and delivery of high-volume data.
- Develop Spark-based ETL/ELT workflows for both batch and real-time streaming data.
- Integrate data from multiple internal and external systems using Kafka, Kinesis, or other streaming frameworks.
- Build and manage data models, warehouses, and lakehouses using AWS services such as S3, Glue, Redshift, Athena, etc.
- Implement data quality checks, validation rules, and monitoring to ensure reliability and consistency.
- Collaborate with data analysts and scientists to provide clean, structured datasets optimized for analytics and ML.
- Work with orchestration tools (Airflow, MWAA, Step Functions, etc.) for automated workflow scheduling.
- Continuously optimize data pipelines for cost, scalability, and performance.
Tech Stack :
- Spark, AWS (S3, Glue, Redshift, Kinesis), Kafka, Airflow, Python, SQL.
Required Skills :
- Strong programming skills in Python for data manipulation and automation.
- Hands-on expertise in Apache Spark (PySpark or Spark SQL) for large-scale data processing.
- Deep understanding of AWS data ecosystem - S3, Glue, Lambda, Redshift, Athena, EMR, Kinesis, IAM.
- Experience with real-time streaming platforms such as Kafka, Kinesis, or Flink.
- Strong command of SQL and data modeling (star schema, dimensional modeling, partitioning).
- Proficiency with data orchestration and workflow management tools (Airflow, Step Functions, etc.).
- Familiarity with Git, CI/CD, and modern development best practices.
- Experience working in Linux/Unix environments and handling large datasets efficiently.
Good to Have :
- Exposure to data lakehouse technologies (Delta Lake, Iceberg, Hudi).
- Understanding of data governance, cataloging, and lineage tools (Glue Data Catalog, Amundsen, DataHub).
- Familiarity with containerization and deployment (Docker, ECS, EKS).
- Basic understanding of AI/ML.
Qualifications :
- Bachelor's degree in Computer Science, IT, Engineering, or related technical field.
- 3 - 4 years of experience in data engineering, big data, or analytics infrastructure.
Skills
Data Engineering, AWS, Data Ingestion, ETL, Kafka, Spark, DataLake, Data Modeling, Data Warehousing, Python, SQL