About the Role

The Data Engineer I role focuses on developing and maintaining ETL pipelines and semantic layers for FBA metrics to support a GenAI-powered insights assistant. This position involves optimizing SQL queries for AI systems and ensuring data quality and freshness through automated testing. The engineer will work on one of Amazon's largest analytics ecosystems, combining massive scale with advanced AI capabilities.

Key Responsibilities

• Develop and maintain ETL pipelines in Spark to transform and load FBA metrics into the Data Lakehouse
• Optimize SQL queries for fast, cost-efficient access by AI systems
• Support dbt semantic models and automate metadata enrichment with Glue
• Write automated tests to ensure data quality and freshness
• Build pipelines that add business context metadata to help AI generate accurate SQL queries

Required Skills & Qualifications

Must Have:

• 1+ years of data engineering experience
• Experience with data modeling, warehousing and building ETL pipelines
• Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
• Experience with one or more scripting language (e.g., Python, KornShell)

Nice to Have:

• Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
• Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
• Proficiency in dbt, Airflow/MWAA, AWS Glue, Kinesis
• Experience building semantic layers or BI models
• Familiarity with prompt-driven SQL generation and AI-assisted query validation