Business Intelligence Engineer, AWS DC Acqn&Construction

About the role

AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. The ML Capacity Delivery Team (MLZ) is responsible for delivering ML and AI infrastructure capacity across AWS’s global data center footprint. As a Business Intelligence Engineer II (BIE) on this team, you will build the data foundations, reporting systems, and analytical capabilities that enable leadership to make critical decisions about how we plan, track, and deliver ML capacity at scale. You will work closely with technical program managers, systems engineers, operations teams, and planning organizations to transform complex operational data into actionable insights that drive delivery velocity and efficiency.

Responsibilities

• Design, develop, and maintain scaled, automated, user-friendly systems, reports, and dashboards that support ML capacity delivery tracking, planning, and operational decision-making.
• Build and optimize data pipelines for extraction, transformation, and loading (ETL) of data from diverse sources across AWS Infrastructure using SQL, Python, and AWS big data technologies.
• Apply deep analytic and business intelligence skills to extract meaningful insights from large and complex data sets related to capacity delivery timelines, supply chain logistics, and infrastructure deployment.
• Collaborate with program managers, systems engineers, and operations teams to understand business requirements and translate them into scalable data solutions and reporting capabilities.
• Build data visualizations that tell the story of ML capacity delivery performance — trends, patterns, bottlenecks, and outliers — through rich, intuitive dashboards for stakeholders at all levels.
• Design and track key performance metrics that measure the health and efficiency of ML capacity delivery operations, including delivery velocity, on-time performance, and pipeline throughput.
• Serve as a liaison between business and technical teams to achieve the goal of providing actionable insights into current business performance and ad hoc analyses to support future improvements or innovations.
• Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.
• Proactively identify opportunities to improve data quality, automate manual reporting processes, and enhance the analytical maturity of the team through predictive and prescriptive analytics.

Requirements

• 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL, etc.
• 1+ years of SQL, ETL, or Oracle experience.
• 1+ years of processing large, multi-dimensional datasets from multiple sources.
• 1+ years of performing statistical analysis.
• 1+ years of developing automated reporting.
• 3+ years of experience in the job offered or a related occupation.
• Bachelor’s degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field.

Qualifications

• Experience with data visualization using Tableau, Quicksight, or similar tools.
• Experience with data modeling, warehousing, and building ETL pipelines.
• Experience in statistical analysis packages such as R, SAS, and Matlab.
• Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling.

Preferred Qualifications

• Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift.
• Experience in data mining, ETL, etc., and using databases in a business environment with large-scale, complex datasets.