Data Engineer - Build Scalable Data Pipelines & Insights

Amazon Inc.; New York (NY); On-site; USD 140,000 - 200,000; Full time

Job summary

Amazon is seeking a Data Engineer to design, build, and optimize data pipelines and architectures that transform raw data into actionable insights. You will collaborate with engineers, data scientists, analysts, and business stakeholders to deliver reliable data solutions powering critical decisions.

You will work on scalable ETL/ELT pipelines, data models, and SQL queries across distributed systems, while exploring automation and AI-enhanced data products to improve quality and discoverability.

Qualifications

• Experience designing and optimising data pipelines at scale.
• Ability to model both logical and physical data schemas.
• Proficient in SQL for large datasets and analytical queries.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines and data infrastructure with a focus on data quality and lineage.
• Create coherent data models that enable reliable storage and retrieval across systems.
• Develop and optimise SQL queries to handle large-scale datasets using distributed processing frameworks.
• Collaborate with analysts, scientists, engineers, and stakeholders to translate requirements into robust data solutions.
• Identify opportunities to automate manual processes and apply generative AI to improve data product quality.

Skills

ETL/ELT pipelines

Data modeling

SQL queries

Data quality & governance

Collaboration

Tools

SQL

Python

Spark

Airflow

Job description

Amazon is seeking a Data Engineer to design, build, and optimize data pipelines and architectures that transform raw data into actionable insights. You will collaborate with engineers, data scientists, analysts, and business stakeholders to deliver reliable data solutions powering critical decisions.

You will work on scalable ETL/ELT pipelines, data models, and SQL queries across distributed systems, while exploring automation and AI-enhanced data products to improve quality and discoverability.