Data Engineer, Ads Data Infrastructure & Pipelines

Amazon Inc.; Seattle (WA); On-site; USD 132,000 - 179,000; Full time

Job summary

Amazon is seeking a Data Engineer to design and optimize data pipelines for Advertising Demand Enablement & Product Ops. You will collaborate with analysts, scientists, and engineers to deliver reliable data solutions and scalable architectures that power critical decision-making.

Responsibilities include building ETL/ELT pipelines, data modeling, and optimizing SQL on large datasets, with opportunities to apply AI tools to enhance data products and productivity.

Qualifications

• 4+ years of data engineering experience.
• Experience building distributed, highly available data pipelines.
• Knowledge of SDLC best practices for software development.
• Knowledge of batch and streaming data architectures like Kafka, Kinesis, Flink, Storm, Beam.
• Bachelor's degree or foreign equivalent in CS/engineering/analytics/statistics/IT or equivalent.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines and data infrastructure.
• Create coherent logical and physical data models that ensure data integrity and efficient storage/retrieval.
• Develop and optimize SQL queries to extract, transform, and manipulate large-scale datasets.
• Collaborate with analysts, scientists, software engineers, and stakeholders to translate requirements into data solutions.
• Identify opportunities to automate data processes and apply AI tools to augment productivity and quality.

Skills

Data engineering

Distributed systems

SQL & data querying

Batch/Streaming architectures

Education

Bachelor's degree or foreign equivalent in CS/engineering/analytics/statistics/IT

Tools

Kafka

Kinesis

Flink

Storm

Beam

Hadoop

Hive

Spark

EMR

Job description

Amazon is seeking a Data Engineer to design and optimize data pipelines for Advertising Demand Enablement & Product Ops. You will collaborate with analysts, scientists, and engineers to deliver reliable data solutions and scalable architectures that power critical decision-making.

Responsibilities include building ETL/ELT pipelines, data modeling, and optimizing SQL on large datasets, with opportunities to apply AI tools to enhance data products and productivity.