Amazon Data Engineer II RISC 

United States, Texas, Arlington 

538544047

Key job responsibilities
1. Design, build, and maintain scalable, fault-tolerant, and efficient data pipelines and infrastructure for machine learning operations (MLOps) leveraging AWS technologies such as Lambda, Glue, EMR/Spark, Step Functions, Airflow, DynamoDB and AWS Batch.
2. Automate infrastructure deployment, maintenance processes, and incorporate CI/CD principles to streamline the MLOps ecosystem, using AWS services and scripting languages like Python or Scala.
3. Develop optimized data models, ETL/ELT processes, data transformations, and data warehouse to ensure high-quality, well-structured data for ML and analytics, using S3, Redshift, Glue, Athena and Lake Formation.
4. Collaborate closely with Applied Scientists, Machine Learning Scientists, and analytics teams to understand data requirements, and provide scalable data solutions.
5. Continuously monitor, optimize, and enhance data pipelines, processes, and infrastructure to support ML and analytics.
6. Implement and enforce rigorous data governance, security, and compliance standards for our data, including data validation, cleansing, and lineage tracking.
7. Mentor junior engineers, promoting best practices and knowledge sharing in data engineering and MLOps.
8. Stay updated with emerging MLOps technologies, tools, and trends, incorporating them into the existing ecosystem for continuous improvement.Work/Life Balance
Mentorship and Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

- Bachelor's degree in computer science, engineering, mathematics, statistics or a related field
- 3+ years of data engineering experience
- Experience with ML
- Experience with data modeling, warehousing and building ETL pipelines
- Knowledge of distributed systems
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence

- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, Step Functions, Airflow, DynamoDB and AWS Batch, SageMaker, 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 advanced ML system design, implementation and maintenance
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Strong problem-solving and engineering skills, with the ability to translate business requirements into technical solutions