Amazon

Data Engineer II

Posted On

July 29, 2025

Location

Bengaluru, Karnataka

Experience Level

Freshers/Experienced

Job Type

Machine Learning Science

Apply By

Aug. 19, 2025

Job Description

Amazon Regulatory Intelligence Safety and Risk (RISC) team mission is to protect customers from products that are unsafe, illegal, illegally marketed, controversial or otherwise in violation of Amazon’s policies while enabling our Selling Partners to offer their broadest selection of safe and compliant products. We achieve these objectives worldwide by: (1) taking a science-first approach to offer trustworthy listings to our customers, (2) inventing intuitive and precise tools to simplify our selling partners’ compliance journey and (3) innovating to reduce our cost to serve.

The RISC Data Engineering team is seeking an experienced Data Engineer with solid engineering skills and machine learning background (MLOps) to join our team. In this role, you will be responsible for designing, building, and maintaining large scale robust data pipelines and infrastructure to empower our machine learning, data science and analytics initiatives. You will collaborate closely with Applied Scientists, Machine Learning Scientists, and business stakeholders to understand their requirements and support AI/ML solutions. Join our expert team to build scalable data solutions, improving Amazon business efficiency and simplifying our selling partners' compliance journey.

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. Adopt genAI solutions to transform and enhance data engineering and MLOps processes.
6. Continuously monitor, optimize, and enhance data pipelines, processes, and infrastructure to support ML and analytics.
7. Implement and enforce rigorous data governance, security, and compliance standards for our data, including data validation, cleansing, and lineage tracking.
8. Mentor junior engineers, promoting best practices and knowledge sharing in data engineering and MLOps.
9. Stay updated with emerging technologies, tools, and trends, incorporating them into the existing ecosystem for continuous improvement.

About the team
Who Are We
We are a team of scientists and engineers building AI/ML and data solutions to improve Amazon business efficiency and simplify our selling partners' compliance journey.

Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

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

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