Business Intelligence Engineer, S&A
DESCRIÇÃO
*Japanese language skill (reading, writing, speaking) is required for this position
Amazon is one of the world’s largest and most customer-centric companies, continuously innovating to make customers’ lives easier. We are seeking a Business Intelligence Engineer to join our dynamic team, driving data-driven decision-making and fostering innovation at scale. Do you want a unique, exciting opportunity to help build something from the ground up? We are looking for builders who thrive in ambiguity to join the BI team of a tech organization under S&A, global partner for Ops Finance.
As a Business Intelligence Engineer at Amazon, you will play a pivotal role in leveraging data to drive insights and support strategic decision-making across the organization. You will be responsible for designing and developing scalable data models, performing statistical analysis, and delivering actionable insights through effective story telling.
The ideal candidate will have a passion for automation and clean, streamlined process design. You will work with analytic tools, write excellent scripts, partner with customers to answer key business questions, and act as an advocate for your customers. Successful members of this team collaborate effectively with internal end-users, cross-functional product teams, and data engineering teams to solve problems, implement new solutions, and deliver successfully against high operational standards of system availability and reliability. We look for candidates who are excellent communicators, self-motivated, flexible, hardworking, and who like to have fun.
Key job responsibilities
- Business level knowledge of Japanese language skill (reading, writing, speaking) is required for this position
- Expertise in SQL for data modeling, and architecture design. Industry experience of 3+ is required.
- Proficiency in Python for data wrangling, EDA, and model evaluation. Industry experience of 3+ years is preferred.
- Industry experience in building and evaluating data models is highly preferred.
- Create compelling data visualizations and dashboards to communicate insights effectively.
- Knowledge of AWS products such as Redshift, QuickSight, or similar experience with other cloud tech stack.
- Collaborate with product, engineering, and business teams to translate ambiguous problems into tech requirements and build data-driven solutions.
- Excellent verbal/written communication & data presentation skills, including ability to succinctly summarize key findings and effectively communicate with both business and technical teams.
About the team
The S&A team plays a crucial role in Operations Finance by leading our data infrastructure transformation. Over the past four years, our business has undergone significant structural changes, creating challenges in managing our data infrastructure effectively.
To address these challenges, we established the S&A team with a clear mission: to align technology solutions with organizational goals through standardization and automation of core finance functions - planning, bridging, reporting, and controllership. Our team's primary objective is to break down existing data silos while preventing new ones from forming. This approach enables faster execution of automation initiatives and promotes standardization across all finance functions. Through centralization, we can reduce duplicate efforts across teams and create more efficient operations throughout Operations Finance.
QUALIFICAÇÕES BÁSICAS
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
QUALIFICAÇÕES PREFERÍVEIS
- 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
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