Data Engineer II, Intl. Seller Growth
Job Description
Come be part of Amazon’s International Seller Services (ISS) Central Analytics Data Engineering team, where we tackle modern data challenges by combining traditional engineering practices with transformative analytics and Generative AI.We are a team of Data Engineers supporting Applied Scientists, Data Scientists, and Economists—turning machine learning, deep learning, and AI research into scalable products that delight our customers worldwide.About the RoleISS is seeking a highly motivated, experienced Data Engineer to help design and build the next generation of Data and ML infrastructure to power GenAI applications.Key job responsibilitiesAs a Data Engineer, you will provide technical leadership, lead data engineering initiatives and build end-to-end analytical solutions that are highly available, scalable, stable, secure, and cost-effective.You are passionate about working with huge datasets and have experience with the organization and curation of data for analytics. You have a strategic and long term view on architecting advanced data eco systems.You are experienced in building efficient and scalable data services and have the ability to integrate data systems with AWS tools and services to support a variety of customer use cases/applications.Design, implement and operate large-scale, high-volume, high-performance data structures for analytics and data science.Implement data ingestion routines using best practices in data modeling, ETL/ELT processes by leveraging AWS technologies and big data tools.Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions with a flexible and adaptable data architecture.Collaborate with engineers to help adopt best practices in data system creation, data integrity, test design, analysis, validation, and documentationIdentify opportunities in existing data solutions for improvements
Locations
- India, TS, Hyderabad, Hyderabad, TS, India
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
2,500,000 - 4,500,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - 3+ years of data engineering experienceintermediate
- - Experience with data modeling, warehousing and building ETL pipelinesintermediate
- - Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)intermediate
- - Experience with one or more scripting language (e.g., Python, KornShell)intermediate
- - Knowledge of writing and optimizing SQL queries in a business environment with large-scale, complex datasetsintermediate
Required Qualifications
- - Bachelor's degree (degree)
- - 3+ years of data engineering experience (experience, 3 years)
- - Experience with data modeling, warehousing and building ETL pipelines (experience)
- - Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala) (experience)
- - Experience with one or more scripting language (e.g., Python, KornShell) (experience)
- - Knowledge of writing and optimizing SQL queries in a business environment with large-scale, complex datasets (experience)
Preferred Qualifications
- - Experience with big data technologies such as: Hadoop, Hive, Spark, EMR (experience)
- - Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc. (experience)
Responsibilities
- As a Data Engineer, you will provide technical leadership, lead data engineering initiatives and build end-to-end analytical solutions that are highly available, scalable, stable, secure, and cost-effective.
- You are passionate about working with huge datasets and have experience with the organization and curation of data for analytics. You have a strategic and long term view on architecting advanced data eco systems.
- You are experienced in building efficient and scalable data services and have the ability to integrate data systems with AWS tools and services to support a variety of customer use cases/applications.
- Design, implement and operate large-scale, high-volume, high-performance data structures for analytics and data science.
- Implement data ingestion routines using best practices in data modeling, ETL/ELT processes by leveraging AWS technologies and big data tools.
- Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions with a flexible and adaptable data architecture.
- Collaborate with engineers to help adopt best practices in data system creation, data integrity, test design, analysis, validation, and documentation
- Identify opportunities in existing data solutions for improvements
