Data Engineer - AWS Analytics & Pricing

Amazon Web Services (AWS)

Seattle (WA)

On-site

USD 132,000 - 179,000

Full time

27 hours ago
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Job summary

Amazon Web Services, Inc. in Seattle, WA is seeking a Data Engineer on the Private Pricing Program to develop scalable data solutions, analytics, and dashboards that inform private pricing decisions.

You will work with GDSP teams and business leaders to translate complex requirements into reliable data pipelines and actionable insights. You'll collaborate across engineering and operations, own data tooling from design to production, and implement best practices for data quality, governance, and

Qualifications

  • 3+ years of data engineering experience.
  • Experience with ETL/ELT processes for BI analytics.
  • Experience with OLAP technologies and data modeling.
  • SQL experience and Oracle experience.

Responsibilities

  • Design, develop, and maintain scalable data solutions for metrics, reports, analyses, and dashboards.
  • Collaborate with technology teams to extract, transform, and load data from diverse sources using AWS services.
  • Build and deliver high-quality datasets for data science and reporting.
  • Automate and optimize reporting processes for self-service analytics.
  • Translate business problems into technical analysis requirements.
  • Develop measurement frameworks for customer experience and business outcomes.

Skills

Data engineering
ETL/ELT
SQL
Data modeling
OLAP
Oracle

Tools

AWS Redshift
Amazon S3
AWS Glue
AWS EMR
AWS Kinesis
AWS Firehose
AWS Lambda
IAM roles

Job description

Amazon Web Services, Inc. in Seattle, WA is seeking a Data Engineer on the Private Pricing Program to develop scalable data solutions, analytics, and dashboards that inform private pricing decisions.

You will work with GDSP teams and business leaders to translate complex requirements into reliable data pipelines and actionable insights. You'll collaborate across engineering and operations, own data tooling from design to production, and implement best practices for data quality, governance, and

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