Sr. Data Engineer, Deal Tooling and Insights, Strategic Customer Engagements

Amazon Amazon · Big Tech · Seattle, WA · Data Science

Senior Data Engineer responsible for building and maintaining backend data infrastructure for analytical platforms and applications, with a focus on leveraging generative AI and AWS services to create data products and feature stores for GenAI applications. This includes end-to-end development of data engineering solutions, architectural improvements, and ensuring data quality.

What you'd actually do

  1. Build and maintain backend data infrastructure for analytical platforms and applications, ensuring data is clean, fresh, and optimized for downstream consumption
  2. Lead architectural improvements that simplify complex data systems and transformations and proactively address deficiencies that create bottlenecks in the data infrastructure
  3. Translate business problem statements into technical data requirements, partnering with BIEs and product management and stakeholders to define what data products to build
  4. Ensure data quality through monitoring, validation, auditing, and documentation of pipelines and data sources
  5. Leverage AWS services and GenAI to build next-generation data solutions that improve efficiency and unlock new analytical capabilities

Skills

Required

  • 5+ years of data engineering experience
  • Experience with data modeling
  • Experience with data warehousing
  • Experience building ETL pipelines
  • Experience with SQL
  • Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
  • Experience mentoring team members on best practices

Nice to have

  • Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
  • Experience operating large data warehouses

What the JD emphasized

  • end-to-end development of data engineering solutions from the ground up
  • proven ability to translate data into meaningful insights
  • design scalable analytics solutions
  • solid understanding of how to build efficient and scalable data infrastructure and data models
  • Build data pipelines purpose-built for LLM consumption
  • create data products and feature stores that serve GenAI applications in near real-time

Other signals

  • Leverage generative AI and AWS services to raise the bar on how the team consumes and acts on data
  • Build data pipelines purpose-built for LLM consumption and create data products and feature stores that serve GenAI applications in near real-time