Data Engineer

Amazon Web Services, Inc.; Austin (TX); On-site; USD 132,000 - 197,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Sign-on payments

Restricted stock units (RSUs)

Adoption and surrogacy reimbursement

Employee Assistance Program (EAP)

Mental health support

Flexible Spending Accounts

Job summary

Amazon Web Services, Inc. in Austin, TX is seeking an on-site Data Engineer to design, build, and operate scalable ETL/ELT pipelines that ingest product telemetry, usage events, and business data across the STT portfolio.

You will join a growing analytics team, work with BI engineers and scientists to shape a centralized data platform using AWS services, implement data models linking usage signals to business outcomes, and contribute to data quality and self-serve analytics for thousands of AWS

Qualifications

• 3+ years of data engineering experience.
• 3+ years designing and operating large scale BI data structures using ETL/ELT processes.
• 3+ years developing and maintaining large scale BI data structures using SQL.
• 3+ years of data modeling experience for BI analytics.
• 3+ years in the job offered or a related occupation.

Responsibilities

• Design, build, and operate scalable ETL and ELT pipelines that ingest product telemetry, usage events, and business outcome data from multiple sources across the STT product portfolio.
• Architect and implement a centralized data platform using AWS native technologies such as Redshift, S3, Glue, Lake Formation, Lambda, and Athena to serve as the single source of truth for analytics.
• Develop data models that connect product usage signals to business outcomes like content effectiveness, field engagement, pipeline progression, and revenue impact.
• Build data infrastructure to support AI and ML pipelines and agentic systems, including MCP tools and natural language data access layers.
• Establish data quality frameworks with automated monitoring, alerting, and validation to ensure accuracy as the platform scales.
• Create self-service data products with clear SLAs, documentation, and governance to reduce ad hoc requests and empower stakeholders.
• Partner with Applied Scientists and SDE teams to provide clean, well modeled data for agent evaluation frameworks, retrieval quality measurement, and content effectiveness scoring.
• Define data contracts, lineage tracking, and catalog metadata to support discoverability and trust.
• Maintain a high bar for operational excellence, including on call ownership, pipeline health monitoring, and proactive resolution of data freshness or quality issues.
• Contribute to the shift from static dashboards to agentic data systems by building foundational data layers that AI agents query and reason over

Skills

Data engineering

ETL/ELT design

SQL

Data modeling

Tools

Redshift

S3

Glue

Lake Formation

Lambda

Athena

EMR

Kinesis

Fire Hose

IAM

Job description

Based in Austin, this on-site Data Engineer role at AWS STT offers the opportunity to design, build, and operate scalable ETL and ELT pipelines along with a centralized data platform that links product telemetry, usage metrics, and business outcomes. The focus is on engineering robust data infrastructure while BI engineers manage reporting and stakeholder analytics to enable self-service insights for thousands of AWS field team members. Salary ranges from USD 132,100 to 196,600 per year.

Benefits

• Health insurance
• 401(k) matching
• Paid time off
• Parental leave
• Sign-on payments
• Restricted stock units (RSUs)
• Adoption and surrogacy reimbursement coverage
• Employee Assistance Program (EAP)
• Mental health support
• Flexible Spending Accounts

Team and Growth

You will join a high growth engineering organization at the forefront of applying generative AI and agentic technologies to transform how AWS field teams operate. The centralized analytics team is being built from the ground up, and you will be among the first two Data Engineers on the team, collaborating with Business Intelligence Engineers, a Senior BD, an Applied Scientist, and a TPM to shape the platform as it scales.

Inclusive Team Culture

Here at AWS, curiosity and a willingness to learn are valued. Employee led affinity groups foster inclusion and pride in our differences. Ongoing events and learning experiences, including Conversations on Race and Ethnicity (CORE) and AmazeCon for gender diversity, encourage everyone to embrace their unique perspectives.

Mentorship & Career Growth

We aim to raise the performance bar and pursue Earth’s Best Employer. The organization emphasizes knowledge sharing, mentorship, and other career-advancing resources to help you develop as a well-rounded professional.

Work/Life Balance

Flexibility and work-life harmony are core to the culture. Support at work and at home enables sustained success in the cloud and beyond.

Responsibilities

• Design, build, and operate scalable ETL and ELT pipelines that ingest product telemetry, usage events, and business outcome data from multiple sources across the STT product portfolio
• Architect and implement a centralized data platform using AWS native technologies such as Redshift, S3, Glue, Lake Formation, Lambda, and Athena to serve as the single source of truth for analytics
• Develop data models that connect product usage signals to business outcomes like content effectiveness, field engagement, pipeline progression, and revenue impact
• Build data infrastructure to support AI and ML pipelines and agentic systems, including MCP tools and natural language data access layers
• Establish data quality frameworks with automated monitoring, alerting, and validation to ensure accuracy as the platform scales
• Create self-service data products with clear SLAs, documentation, and governance to reduce ad hoc requests and empower stakeholders
• Partner with Applied Scientists and SDE teams to provide clean, well modeled data for agent evaluation frameworks, retrieval quality measurement, and content effectiveness scoring
• Define data contracts, lineage tracking, and catalog metadata to support discoverability and trust
• Maintain a high bar for operational excellence, including on call ownership, pipeline health monitoring, and proactive resolution of data freshness or quality issues
• Contribute to the shift from static dashboards to agentic data systems by building foundational data layers that AI agents query and reason over

Requirements

• 3+ years of data engineering experience
• 3+ years designing and operating large scale BI data structures using ETL/ELT processes
• 3+ years developing and maintaining large scale BI data structures using SQL
• 3+ years of data modeling experience for BI analytics
• 3+ years in the job offered or a related occupation

Technologies

• Redshift
• S3
• Glue
• Lake Formation
• Lambda
• Athena
• EMR
• Kinesis
• Fire Hose
• IAM