Data Engineer II, AR Data Egineering, FinAuto
Amazon Prime Video · Not specified · onsite · Posted 41d ago
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About this role
Description
After building a proven Data Mesh architecture serving thousands of users, we are re-imagining our data ecosystem as an AI-First, Native-AI platform that powers the next generation of intelligent financial automation. At the heart of this transformation is Graphite - our AI Enablement Layer that makes financial data products discoverable, queryable, and consumable by AI agents and human users. The FinAuto team at Amazon is looking for a Data Engineer to play a critical role in building AI-ready, high-scale financial data products that fuel Graphite-powered self serve insights and data-driven decision making at enterprise scale.
This is a unique opportunity to help shape a our data foundation - building highly discoverable, governed, and reusable data products that power analytics, machine learning, generative AI, and agentic applications across Amazon Finance. Your work will directly feed into Graphite's ability to orchestrate AI-driven data access - ensuring datasets are semantically rich, well-governed, and optimized for both human analysts and AI agents. You will modernize our architecture using technologies such as Zero ETL, AWS DataZone, end-to-end lineage, real-time observability, and automated data quality frameworks.
The ideal candidate is passionate about building next-generation distributed data systems on AWS and believes in democratizing access to high-quality data - whether consumed through dashboards, APIs, or AI-native interfaces. You will work on scalable, secure, and AI-compatible data platforms that enable self-service analytics, semantic data discovery, and intelligent financial operations.
Key job responsibilities
1. Build and maintain scalable, reliable data pipelines and datasets on AWS (S3, EMR, Redshift, Glue) to support Finance analytics, reporting, and AI-powered financial automation
2. Develop and enhance AI-ready data products, ensuring high quality, usability, clear data definitions, and semantic richness to support consumption by both human analysts and Graphite-powered AI agents
3. Implement robust ETL/ELT workflows and contribute to modern patterns such as Zero ETL, data mesh, and standardized ingestion frameworks that reduce data movement and accelerate time-to-insight
4. Ensure end-to-end data quality, observability, and governance through validation, monitoring, lineage, and metadata management (e.g., AWS DataZone), building trust in datasets across the organization
5. Collaborate with cross-functional teams (analytics, finance, data science, ML engineering) to translate business needs into scalable, well-modeled data solutions
6. Drive Operational Excellence through Full CD Pipeline adoption and AI-first approaches - including proactive anomaly detection, automated root cause analysis, and self-healing workflows - to shift from reactive operations to predictive, autonomous data platform reliability
About the team
The AR Data Engineering (ARDE) team builds the data foundation that powers every Accounts Receivable decision at Amazon - trusted, near real-time, and self-serve. We develop entity-centric data products spanning collections, cash management, customer contacts, and billing, enabling FinOps leaders, automation platforms, and analytics partners to discover, access, and act on AR data independently. We are evolving from a proven Data Mesh serving thousands of users into a cognitive data layer - powered by Graphite - that compounds institutional knowledge, accelerates decision velocity, and drives operational autonomy across Amazon Finance.
Basic Qualifications
- 3+ years of data engineering experience
- 4+ years of SQL 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)
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