Data Engineer I: GenAI-Driven Analytics & Pipelines

Amazon Inc.; Asti; Ibrido; EUR 65.000 - 90.000; Tempo pieno

Descrizione del lavoro

Amazon DSP Analytics seeks a Data Engineer to design and run robust data pipelines for the DSP program. You’ll collaborate with scientists, economists, and analysts to translate ML research into scalable data products and GenAI-driven workflows.

You will craft end-to-end data infrastructure, ensure security and quality, and help optimize performance at scale across real-time and batch workloads.

Competenze

• 1+ years of data engineering experience.
• Experience with SQL.
• Experience with data modeling, warehousing and ETL pipelines.
• Experience with one or more query languages (e.g., SQL, HiveQL, SparkSQL).
• Experience with scripting language (e.g., Python).
• Knowledge of database, data warehouse, or data lake solutions.

Mansioni

• Design, build, and maintain data pipelines from diverse sources.
• Develop and optimize ETL processes to clean and structure data for analysis.
• Implement and manage data warehousing solutions for performance.
• Establish data quality standards and validate data integrity.
• Optimize pipelines for large data volumes and reliability.
• Ensure data privacy and security through access controls and encryption.
• Collaborate with data scientists and analysts to meet data needs.
• Maintain documentation for pipelines, models, and processes.
• Implement monitoring to detect pipeline failures and bottlenecks.
• Keep up with trends to improve data infrastructure.

Conoscenze

Data engineering

SQL

ETL pipelines

Data modeling

Data warehousing

Python

Strumenti

AWS

Hadoop

Spark

Hive

EMR

Redshift

S3

Glue

Kinesis

Descrizione del lavoro

Amazon DSP Analytics seeks a Data Engineer to design and run robust data pipelines for the DSP program. You’ll collaborate with scientists, economists, and analysts to translate ML research into scalable data products and GenAI-driven workflows.

You will craft end-to-end data infrastructure, ensure security and quality, and help optimize performance at scale across real-time and batch workloads.