Data Engineer II, , GFS (Global Finance Solution)

Data Engineer II, GFS (Global Finance Solution)

Amazon is a US-based multinational electronic commerce company headquartered in Seattle, Washington. Amazon.com started as an online bookstore, but soon diversified into many other categories, with a vision to be Earth's most customer‑centric company and to build a place where people can come to find and discover anything they might want to buy online.

Amazon's Global Finance Services (GFS) team is a fast‑paced, team‑focused, dynamic environment where delivering great experiences for our customers is a top priority. GFS is seeking a Business Intelligence Engineer to support our Amazon Music finance team.

As a Data Engineer, you will play a key role in transforming the way GFS teams access and analyse the data. You will work to advance the 3‑Year Data Infrastructure Modernisation strategy and play a key role in adopting and expanding a unified data access platform and scalable governance and observability frameworks that follow modern data architecture and cloud‑first designs.

Your responsibilities include supporting and migrating data analytics use cases for a targeted customer group, implementing new features within the central platform, designing components and using AWS services that follow best engineering practices, and conducting user acceptance testing, launch, adoption and post‑launch support. You will work on system design, integrate new components into the established architecture, collaborate across teams by building reusable design patterns, and contribute to "buy" vs "build" decisions by evaluating product releases, performing gap analysis, and defining feasibility and blockers.

Key job responsibilities

• Elevate and optimise existing solutions while driving strategic migration. Conduct thorough impact assessments to identify opportunities for transformative re‑architecture or migration to central platforms.
• Design, review and implement data solutions that support WW Operations Finance standardisation and automation initiatives using AWS technologies and internally built tools (Spark/EMR, Redshift, Athena, DynamoDB, Lambda, S3, Glue, Lake Formation, etc.).
• Support data solutions adoption by finance and technical teams; identify and remove adoption blockers.
• Ensure the speed of delivery and high quality: iteratively improve the development process and adopt mechanisms for optimisation of development and support.
• Contribute to engineering excellence by reviewing design and code created by others.
• Contribute to delivery execution, planning, operational excellence, retrospectives, problem identification, and solution proposals.
• Collaborate with finance analysts, engineers, product and program managers and external teams to influence and optimise the value of the delivery in the data platform.
• Create technical and customer‑facing documentation on the products within the platform.

A day in the life

You work with the Engineering, Product, BI and Operations teams to elevate existing data platforms and implement best‑of‑class data solutions for WW Operations Finance. You solve unstructured customer pain points with technical solutions, focusing on user productivity. You participate in discussions with stakeholders to provide updates on project progress, gather feedback, and align on priorities. Utilizing AWS CDK and various AWS services, you design, execute, and deploy solutions. Your broader focus is on system architecture rather than individual pipelines. You regularly review your designs with Principal Engineers, incorporate gathered insights, and establish efficient development and change‑management processes to guarantee speed, quality, and scalability of delivered solutions.

Basic Qualifications

• 3+ years of data engineering experience
• Experience with data modelling, warehousing and building ETL pipelines

Preferred Qualifications

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

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