Job Overview

Amazon is hiring a Data Engineer II for the International Seller Services Central Science and Analytics team in Karnataka. This role focuses on building reliable data and machine learning infrastructure that supports applied scientists, data scientists, and economists working on analytical and AI-driven solutions.

The position is suited to an experienced data engineering professional who enjoys working with large datasets and designing systems that can support demanding analytics workloads. You will help create end-to-end data solutions that are scalable, highly available, secure, stable, and cost-conscious. The work combines data architecture, data modeling, ingestion, ETL and ELT development, cloud technologies, and operational engineering.

A major part of the role involves turning business and functional requirements into practical data architectures. You will work with engineering and science teams to improve data quality, strengthen existing solutions, and create infrastructure that can support a variety of analytical and customer-focused use cases.

Key Responsibilities

• Design and implement large-scale data structures that support high-volume analytics and data science workloads.
• Build and operate reliable data services with an emphasis on scalability, availability, performance, security, and cost efficiency.
• Develop data ingestion workflows using appropriate data modeling and ETL or ELT practices.
• Use AWS technologies and big data tools to create robust data pipelines and analytical infrastructure.
• Translate business and functional requirements into scalable and maintainable data architecture.
• Work with engineering teams to improve data integrity, testing, validation, documentation, and development practices.
• Review existing data solutions and identify opportunities for performance, reliability, maintainability, or efficiency improvements.
• Support data engineering initiatives from design and implementation through ongoing operation.
• Help create data environments that enable scientists and analysts to experiment with machine learning, deep learning, and AI solutions.
• Apply a long-term architectural perspective when designing data ecosystems.
• Integrate cloud services and data systems to support different analytical and application requirements.

Required Skills

Candidates should have at least 3 years of professional data engineering experience and at least 4 years of SQL experience. Strong practical knowledge of data modeling, data warehousing, and ETL pipeline development is required.

A strong candidate should understand how data moves through modern analytical platforms, from ingestion and transformation to storage, validation, and consumption. Experience working with large datasets and building dependable data pipelines is important for this role.

The position also requires an engineering mindset. Candidates should be comfortable thinking about reliability, scalability, data integrity, operational support, and maintainability rather than focusing only on individual pipeline development tasks.

Preferred Skills

Experience with AWS data and analytics services is strongly relevant. The job description specifically identifies Amazon Redshift, Amazon S3, AWS Glue, Amazon EMR, Amazon Kinesis, Kinesis Data Firehose, AWS Lambda, and IAM roles and permissions.

Experience with non-relational data stores is also useful. This may include object storage, document databases, key-value stores, graph databases, and column-family databases.

Candidates with knowledge of professional software engineering practices can be well suited to the role. Relevant practices include coding standards, software architecture, code reviews, source control management, continuous deployment, testing, and operational excellence.

Experience with Apache Airflow, AWS Step Functions, or other workflow orchestration frameworks is preferred.

Education

The supplied job description does not state a specific mandatory degree or educational qualification. Candidates should therefore focus on demonstrating the required professional data engineering experience, SQL expertise, data modeling capability, and ETL development background.

Experience

A minimum of 3 years of data engineering experience is required. The role also specifies at least 4 years of SQL experience, along with practical experience in data modeling, data warehousing, and building ETL pipelines.

Candidates with additional experience designing scalable analytical platforms, integrating AWS data services, working with large datasets, and supporting data science or machine learning workloads may be particularly relevant.

Required Technologies

• SQL
• Data Modeling
• Data Warehousing
• ETL
• ELT
• AWS
• Amazon Redshift
• Amazon S3
• AWS Glue
• Amazon EMR
• Amazon Kinesis
• Kinesis Data Firehose
• AWS Lambda
• AWS IAM
• Non-relational Databases
• Object Storage
• Document Databases
• Key-Value Stores
• Graph Databases
• Column-Family Databases
• Apache Airflow
• AWS Step Functions
• Workflow Orchestration

Soft Skills

Strong problem-solving and analytical thinking are important because the role involves translating business requirements into technical data solutions. Candidates should be able to understand complex data needs, evaluate architectural options, and create practical solutions that can operate reliably at scale.

Collaboration is also essential. The Data Engineer will work with software engineers, applied scientists, data scientists, economists, and other stakeholders. Clear communication and the ability to explain technical decisions can help teams align on data architecture and delivery priorities.

A strategic mindset is valuable for this role. The position involves designing advanced data ecosystems with a long-term view, while also identifying practical improvements in existing data solutions.

Benefits of Working in this Role

This role provides exposure to large-scale data engineering and cloud-based analytics within Amazon's International Seller Services organization. Engineers can work closely with science and engineering teams that use data infrastructure for research, experimentation, machine learning, and AI-related product development.

The position also offers opportunities to strengthen skills in AWS data services, scalable data architecture, ETL and ELT pipelines, data modeling, warehousing, workflow orchestration, and operational engineering. Working on high-volume data environments can provide valuable experience in designing systems for reliability, performance, and scale.

Work Mode

The supplied job description does not specify whether this position is onsite, hybrid, or remote. Candidates should verify the current work arrangement and office requirements with Amazon during the application process.

Location

This position is listed under ADCI - Karnataka - A66. The supplied job description does not provide a specific city, so Karnataka, India is used as the location without assuming a particular city.

Who Should Apply

This opportunity is suitable for experienced Data Engineers, Analytics Engineers, Cloud Data Engineers, and professionals who have built scalable data pipelines and analytical data platforms.

Candidates with 3 or more years of data engineering experience and strong SQL expertise should consider applying. Experience with data modeling, data warehousing, ETL pipelines, AWS services, and large datasets will be especially relevant.

Professionals moving toward data architecture, cloud data engineering, or data platform engineering may also find the role aligned with their career goals, provided they meet the stated experience requirements.

Career Growth

The role can help experienced data engineers expand their capabilities across data architecture, cloud infrastructure, analytics engineering, and machine learning support. Working with scientists and engineers can provide broader exposure to how data platforms enable experimentation and customer-facing solutions.

The opportunity to work on large-scale data systems can also strengthen skills in scalability, reliability, security, operational excellence, and long-term architecture. Engineers who take ownership of data engineering initiatives can develop stronger technical leadership and solution design capabilities.

Application Advice

When applying for this Amazon Data Engineer II opportunity, make your data engineering experience easy to verify. Clearly highlight your years of experience with data engineering and SQL, along with projects involving data modeling, data warehousing, ETL or ELT pipelines, and large datasets.

If you have worked with Amazon Redshift, S3, AWS Glue, EMR, Kinesis, Firehose, Lambda, IAM, Airflow, or Step Functions, list the services and explain how you used them. Include measurable examples where possible, such as improvements in pipeline reliability, processing performance, data quality, cost efficiency, or delivery time.

Also mention experience with non-relational data stores and software engineering practices such as code reviews, source control, testing, continuous deployment, and operational support when applicable.

Review the official Amazon listing for the latest application and location details before applying.