Job Description
After the successful on boarding on data mesh architecture serving thousands of users, we are excited to further evolve our data products to make it AI ready. Finance Automation team at Amazon is looking for Data Engineer to play a key role in building next generation Financial data warehouse. Join us if you would like to be part of the exciting journey of further modernizing our architecture to be AI ready, leveraging Zero ETL, AWS Data Zone with end to end data lineage and 100% data quality visibility. The ideal candidate will be passionate about building next generation extremely large, scalable and fast distributed systems on AWS stack and will want to be part of a team that has accepted the goal to democratize access to data and enabling data driven innovations for entire Finance business in Amazon. We are one of fastest growing Data Engineering team across Amazon with strong technology orientation.
Looking for a candidate with strong background in new age AWS stack (S3, EMR, Redshift) or traditional BI & DW with interest in data mining and ability to sieve emerging patterns and trends from large amount of data. Data Engineer should have strong experience with all standard data warehousing technical components (e.g. ETL, Reporting, and Data Modeling), infrastructure (e.g. hardware and software) and their integration. The ideal candidate will have extensive experience in dimensional modeling, excellent problem solving ability dealing with huge volumes of data and a short learning curve. Excellent written and verbal communication skills are required as the candidate will work very closely with diverse teams and senior leadership.
Join our exceptional team where you'll tackle challenging problems while working alongside industry-leading data engineering talent. We offer a premium work environment where you can make meaningful contributions, shape the future of data engineering, and enjoy the journey along the way.
Key job responsibilities
Strong understanding of ETL concepts and experience building them with large-scale, complex datasets using traditional or map reduce batch mechanism.
Good data modelling skills with knowledge of various industry standards such as dimensional modelling, star schemas etc
Proficient in writing performant SQL working with large data volumes
Experience designing and operating very large Data Warehouses
Experience with scripting for automation (e.g., UNIX Shell scripting, Python).
Good to have - experience working on AWS stack? Clear thinker with superb problem-solving skills to prioritize and stay focused on big needle movers ? Curious, self-motivated & a self-starter with a ?can do attitude?. Comfortable working in fast paced dynamic environment.
About the team
The AR Data Engineering (ARDE) team develops and maintains robust data solutions that power Global Accounts Receivable operations. We build comprehensive datasets for collections, cash management, customer contacts, and billing processes that drive critical business insights. Our scalable platform transforms how AR teams operate by providing near-time visibility into cash flow and collections, optimizing working capital management, enhancing customer experience, and enabling secure, efficient data access and analysis. Through our sophisticated data infrastructure, we ensure AR teams can easily discover, access, and analyze the right information at the right time, empowering better decision-making across the organization."
Basic Qualifications
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)
Preferred Qualifications
Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
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
https://amazon.jobs/content/en/how-we-hire/accommodations
for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
Looking for a candidate with strong background in new age AWS stack (S3, EMR, Redshift) or traditional BI & DW with interest in data mining and ability to sieve emerging patterns and trends from large amount of data. Data Engineer should have strong experience with all standard data warehousing technical components (e.g. ETL, Reporting, and Data Modeling), infrastructure (e.g. hardware and software) and their integration. The ideal candidate will have extensive experience in dimensional modeling, excellent problem solving ability dealing with huge volumes of data and a short learning curve. Excellent written and verbal communication skills are required as the candidate will work very closely with diverse teams and senior leadership.
Join our exceptional team where you'll tackle challenging problems while working alongside industry-leading data engineering talent. We offer a premium work environment where you can make meaningful contributions, shape the future of data engineering, and enjoy the journey along the way.
Key job responsibilities
Strong understanding of ETL concepts and experience building them with large-scale, complex datasets using traditional or map reduce batch mechanism.
Good data modelling skills with knowledge of various industry standards such as dimensional modelling, star schemas etc
Proficient in writing performant SQL working with large data volumes
Experience designing and operating very large Data Warehouses
Experience with scripting for automation (e.g., UNIX Shell scripting, Python).
Good to have - experience working on AWS stack? Clear thinker with superb problem-solving skills to prioritize and stay focused on big needle movers ? Curious, self-motivated & a self-starter with a ?can do attitude?. Comfortable working in fast paced dynamic environment.
About the team
The AR Data Engineering (ARDE) team develops and maintains robust data solutions that power Global Accounts Receivable operations. We build comprehensive datasets for collections, cash management, customer contacts, and billing processes that drive critical business insights. Our scalable platform transforms how AR teams operate by providing near-time visibility into cash flow and collections, optimizing working capital management, enhancing customer experience, and enabling secure, efficient data access and analysis. Through our sophisticated data infrastructure, we ensure AR teams can easily discover, access, and analyze the right information at the right time, empowering better decision-making across the organization."
Basic Qualifications
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)
Preferred Qualifications
Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
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
https://amazon.jobs/content/en/how-we-hire/accommodations
for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
Key skill Required
- SQL
- Shell Scripting
- Architecture
- Python
- AWS
- Automation
- Data Engineering
- Data Lineage
- Accounts
- Accounts Receivable
- Analysis
- Big Data
- Billing
- Business Insights
- Can Do Attitude
- Capital Management
- Cash Management
- Communication
- Comprehensive
- Customer Experience
- Data Access
- Data Driven
- Data Infrastructure
- Data Mining
- Data Modeling
- Data Quality
- Data Warehouse
- Dimensional Modeling
- Dynamic Environment
- Etl Tool
- Finance
- Financial Data
- Focused
- Inclusive
- Industry Standards
- Infrastructure
- Integration
- Leadership
- Learning
- Management
- Mining
- Onboarding
- Orientation
- Passionate
- PL/SQL
- Problem Solving
- Query Language
- Reporting
- Scripting Language
- SPARK
- Unix Shell
- Verbal Communication
- Working Capital Management
- Writing
- Written and Verbal Communication
- Finance Automation
