Data Engineer, AWS GDSP A&I

About Amazon Web Services (AWS)

Amazon Web Services (AWS) is a dynamic and rapidly growing business within Amazon and the leader in providing secure, reliable, scalable, and innovative services that help over a million businesses, governments, education, and not for profits across the globe scale and grow! AWS provides a wide set of scalable services to meet customer needs.

At AWS, the Global Deal Strategy and Programs (GDSP) team drives cloud adoption and business growth through innovative pricing strategies. The organization comprises two specialized teams: Strategic Customer Engagements, which guide transformative deals with industry leaders, and Private Pricing Programs & Experiences, which scales and optimizes pricing solutions across our diverse customer base. Within GDSP, you will develop deep expertise in cloud economics, hone your strategic thinking, and directly impact AWS's market leadership while working with latest technologies and global clients.

About the Role

The AWS Global Deal Strategy and Programs (GDSP) organization is responsible for the Private Pricing Program. The Private Pricing Analytics and Insights (PPA&I) team owns building scalable analytical solutions that enable the GDSP organization with actionable insights to make data-driven decisions. This role will focus on Data Engineering, and Analytics related to the Private Pricing Program, requiring deep technical skills, strong business acumen and a deep analytical background to provide actionable data-driven insights and decision support.

As a Data Engineer on this team, you will drive efficiency in data handling processes, setup product analytics, drive advanced analysis and build new metrics which are key inputs to improve the health, scale and growth of the Private Pricing Program. You will work collaboratively with the business leaders operations, and engineering teams on many non-standard and unique business problems and support business initiatives by collecting required and related data from external/internal sources.

The ideal candidate should have strong communication skills and ability to prioritize effectively to ensure timelines are met. You will own projects from engaging with customers to production and delivery of a suite of tools used by the organization to make key business decisions. You should be a self-starter, comfortable with ambiguity, able to think big and be creative (while still paying careful attention to detail). You think in terms of architecture, not just code. You proactively work to improve the consistency and integration between your team’s BI solutions and any related systems or artifacts.

Key job responsibilities

• Design, develop, and maintain scalable data solutions for metrics, reports, analyses, and dashboards that support analytical and business needs
• Collaborate with technology teams to extract, transform, and load data from diverse sources using AWS services and internal tools
• Build and deliver high-quality datasets that support data science initiatives and customer reporting requirements
• Automate and optimize reporting processes to enable self-service analytics for stakeholders
• Translate business problem statements into technical analysis requirements
• Apply analytical and statistical rigor to answer business questions and drive data-driven decisions
• Develop measurement frameworks that quantify customer experience and drive business outcomes
• Create queries and visualizations for ad-hoc requests, projects, and ongoing reporting needs
• Write efficient, optimized queries with deep knowledge of data structures and query optimization techniques
• Adopt best practices in reporting and analysis including data integrity, design, validation, and documentation
• Troubleshoot operational data quality issues and audit existing data pipelines and queries
• Recommend improvements to back-end data sources for increased accuracy and simplicity

Basic Qualifications

• 3+ years of data engineering experience
• 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
• 1+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies experience
• 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
• 1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience
• 1+ years of developing and operating large-scale data structures for business intelligence analytics using Oracle experience
• Experience with data modeling, warehousing and building ETL pipelines

Preferred Qualifications

• Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
• Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

Key skills/competency

• Data Engineering
• AWS
• ETL/ELT
• SQL
• Data Modeling
• Business Intelligence
• Analytics
• Data Warehousing
• Problem Solving
• Communication