Key job responsibilities
* Design, build, and maintain scalable ETL pipelines and data solutions using Python, SQL, Spark, and AWS services (S3, Glue, Redshift, Airflow, EMR, Lambda)
* Optimize query performance and resource usage across the data platform, participate in code reviews and design discussions, and identify process improvements that increase automation and reduce manual effort
* Document solutions thoroughly to ensure maintainability, validate outputs against source data and business logic, and handle data in accordance with Amazon policies
* Develop AI-powered dashboards and intelligent agents that automate metrics bridging, commentary generation, and deep-dive queries, enabling finance users to make faster data-driven decisions
Collaborate with finance stakeholders, data engineers, and product teams to gather requirements, integrate data feeds from multiple sources, and ensure datasets are accurate, accessible, and aligned with evolving business needs
A day in the life
You start your morning reviewing pipeline health dashboards and triaging data quality alerts on cost data flowing across supply chain legs. By mid-morning, you are collaborating with finance analysts to reconcile discrepancies between finance, stores, and operations datasets. After lunch, you work with product teams and finance partners to define cost estimation logic for newly launched business lines, warehouse types, and carrier nodes, ensuring the allocation approach is consistent across markets. You then build and test the ETL pipeline to support it. You wrap up the day with a design session on upcoming datasets, ETL pipelines, dashboards or conduct code reviews to make sure that our solution scales across all regions.
About the team
Our team within Operations Finance builds and operates the unified data platform that powers financial reporting, bridging, and analytics for over 100 finance customers across all Rest of World (RoW) countries. We support 10+ data producer teams and are focused on scaling automation, improving data quality, and incorporating AI-driven workflows for both our builders and our customers. If you want to shape how a global finance organization interacts with data, this is the place to do it.
Basic qualifications
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience- Experience with data visualization software (e.g., AWS QuickSight or Tableau) or open-source project
- Experience with one or more scripting language (e.g., Python, KornShell)
- Require 3+ years of creating production quality data pipelines along and exposure to data modelling
Preferred qualifications
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR- Master's degree
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