Data Engineer I, UTR PLanning Tech
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Job Location:
Nashville, IN - USA
Monthly Salary:
Not Disclosed
Posted on:
2 hours ago
Vacancies:
1 Vacancy
Department:
Job Summary
Our team is shifting from hand-coded custom pipelines to an AI-native approach. We are building reusable frameworks where engineers and business users define what they need through configurations and natural language instead of writing custom code for every use case. AI agents handle orchestration validation and deployment. We are early in this transformation which means you will not inherit a finished system. You will help define how we build what patterns we standardize and how AI fits into data engineering workflows. If you want to learn fast and have a hand in shaping the way a team works this is that opportunity.
We are hiring a Data Engineer I to build data pipelines contribute to these frameworks and help shape how AI transforms data engineering at Amazon.
Key job responsibilities
* Design and implement physical data models and ETL pipelines using AWS services (Redshift S3 EMR Glue Lambda) and Python-based orchestration (Airflow/MWAA).
* Build configuration-driven data frameworks that replace repetitive custom code with reusable declarative patterns for ingestion transformation and metric curation.
* Develop and optimize pipelines that ingest from multiple upstream sources apply quality controls and produce datasets that scientists and analysts use for labor planning decisions.
* Implement monitoring alarming and data quality controls using standardized data contracts. Measure and improve dataset quality.
* Build tooling that enables AI agents to interact with data engineering workflows: generating SQL validating configurations and executing pipeline operations through natural language interfaces.
* Collaborate with planning scientists software engineers BI engineers and product managers to translate data requirements into reusable solutions.
* Write secure testable maintainable code. Get your data models pipeline designs and code reviewed. Document your solutions so others can maintain them.
* Participate in operational excellence practices: on-call rotations COE reviews and pipeline health monitoring.
- 1 years of data engineering experience
- Experience with data modeling warehousing and building ETL pipelines
- Experience with one or more query language (e.g. SQL PL/SQL DDL MDX HiveQL SparkSQL Scala)
- Experience with one or more scripting language (e.g. Python KornShell)
- Experience with big data technologies such as: Hadoop Hive Spark EMR
- Experience with any ETL tool like Informatica ODI SSIS BODI Datastage etc.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.
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 TN Nashville - 96200.00 - 160000.00 USD annually
USA TX Austin - 101300.00 - 160000.00 USD annually
USA WA Bellevue - 101300.00 - 160000.00 USD annually
Required Experience:
IC
Key Skills
- Apache Hive
- S3
- Hadoop
- Redshift
- Spark
- AWS
- Apache Pig
- NoSQL
- Big Data
- Data Warehouse
- Kafka
- Scala
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