Data Engineer I — Build Large-Scale Security Data Pipelines

Amazon; Seattle (WA); On-site; USD 101,000 - 160,000; Full time

Job summary

Amazon's Veritas team is seeking a Data Engineer I to build and maintain data pipelines for security investigations and threat modeling. You’ll collaborate across roles to gather data requirements, partner with software engineers, and shape scalable data infrastructure using AWS services like EMR, Glue, Athena, and Redshift.

You will work in a fast-paced environment focused on data quality, governance, and AI-driven data object creation across a lake of hundreds of datasets, with exposure to

Qualifications

• 1+ years of data engineering experience.
• Experience with data modeling, warehousing and ETL pipelines.
• Experience with one or more query languages (SQL, HiveQL, SparkSQL, etc.).
• Experience with one or more scripting languages (Python, KornShell).
• Experience with big data technologies such as Hadoop, Hive, Spark, EMR.

Responsibilities

• Develop world class data pipelines by working with customers.
• Create designs for data governance strategy and data schemas.
• Improve data quality and operations via automation and CI/CD pipelines.
• Adopt best practices in data integrity, model, testing and documentation.
• Build end-to-end data pipelines to ingest and transform data from diverse sources.
• Utilize AI/LLM to automate manual data engineering tasks.
• Evaluate big-data technologies to optimize processing of large datasets.
• Write high performing, optimized SQL queries.

Skills

Data engineering

Data modeling

ETL pipelines

SQL / HiveQL / SparkSQL

Python scripting

Big data tech

Job description

Amazon's Veritas team is seeking a Data Engineer I to build and maintain data pipelines for security investigations and threat modeling. You’ll collaborate across roles to gather data requirements, partner with software engineers, and shape scalable data infrastructure using AWS services like EMR, Glue, Athena, and Redshift.

You will work in a fast-paced environment focused on data quality, governance, and AI-driven data object creation across a lake of hundreds of datasets, with exposure to