As a Data Engineer on this team, you will build the data foundation that measures how these products are helping us deliver customer service at scale, and how we can further improve them.
The future of customer service depends on how effectively associates and AI systems work together, with associates applying the judgment, empathy, and context that customers need most. Making that partnership work starts with answering three questions with confidence: Are we delivering service we're proud of? Did we resolve the customer's problem? Are associates set up to do their best work? Answering them at scale requires the datasets, pipelines, and data contracts you will design and own.
Your datasets become the source of truth for how Amazon Customer Service measures and improves itself.
Key job responsibilities
- Design and build scalable ETL/ELT pipelines that ingest billions of daily interaction events from diverse sources, using AWS technologies such as Redshift, Glue, EMR, Kinesis, Lambda, and S3
- Design data models and schemas that make high-volume event data documented, queryable, and performant for analytics, science, and AI use cases
- Define and enforce data contracts, quality checks, and freshness SLAs adopted by engineering and science teams across the organization
- Build ML-ready datasets and feedback loops that power automated quality measurement, AI model training, and continuous improvement of associate tools
- Own monitoring, alerting, and observability for your pipelines, proactively identifying and resolving data quality issues before consumers are impacted
- Modernize existing data infrastructure, proposing architectural improvements that increase reliability, reduce cost, and improve performance
- Break down ambiguous business questions into concrete data deliverables, partnering with software engineers, applied scientists, and business intelligence engineers
- Use GenAI tools to automate pipeline operations and accelerate your own development workflows
A day in the life
You might start the day checking pipeline health and fixing a data freshness issue before anyone downstream notices. Mid-day, you define the data contract for events a new associate-facing feature will emit, so the data arrives documented and usable. You close the day proposing a simplification to a legacy pipeline. Your stakeholders are software engineers, applied scientists, and business intelligence engineers; your customers are the customer service associates whose tools improve because of what your data reveals.
About the team
We are a multidisciplinary team of data engineers and scientists within the Associate Experience organization in Amazon Customer Service. Our organization builds the technology customer service associates use every day: the contact handling workspace, AI-assisted resolution tools, and the systems that measure service quality. Our team owns three connected charters: data engineering for the entire organization, contact routing for driver support experiences, and the science and AI capabilities that power both. As a data engineer here, you will sit alongside scientists and build the datasets that engineering, science, and operations teams rely on.
BASIC QUALIFICATIONS
- 3+ years of data engineering experience
- 2+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
- 3+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
PREFERRED QUALIFICATIONS
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with data modeling, warehousing and building ETL pipelines
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 132,100.00 - 178,800.00 USD annually
