DESCRIPTION : After building a proven Data Mesh architecture across 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. 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 enable autonomous insights, intelligent workflows, and data-driven decision making at enterprise scale.

- Strong expertise in core data engineering fundamentals including ETL/ELT, distributed processing, data modeling, orchestration, and reporting systems
- Experience building scalable and reliable data pipelines handling massive datasets with high availability and performance requirements
- Good understanding of dimensional modeling, data governance, lineage, observability, and data quality frameworks
- Familiarity with AI/ML data requirements including feature-ready datasets, vectorizable data structures, metadata management, and semantic discoverability
- Ability to think in terms of reusable data products instead of siloed pipelines
- Excellent problem-solving abilities, ownership mindset, and a passion for simplifying complex data ecosystems
- Strong written and verbal communication skills with experience partnering across engineering, finance, product, and senior leadership teams

Join our exceptional team where you'll solve complex data challenges while working alongside industry-leading engineers building Amazon’s AI-First finance data ecosystem. You will have the opportunity to influence the future of enterprise-scale data products, contribute to Native-AI platform modernization, and build systems that power intelligent automation for one of the world’s largest FinOps organizations.

Key job responsibilities

- Build and maintain scalable, reliable data pipelines and datasets on AWS (S3, EMR,



Redshift, Glue) to support Finance analytics and reporting
- Develop and enhance AI-ready and analytics-ready data products, ensuring high quality, usability, and clear data definitions
- Implement robust ETL/ELT workflows and contribute to contemporary patterns such as Zero ETL, data mesh, and standardized ingestion frameworks
- Ensure end-to-end data quality, observability, and governance through validation, monitoring, lineage, and metadata management (e.g., AWS DataZone)
- Collaborate with cross-functional teams (analytics, finance, data science) to translate business needs into scalable, well-modeled data solutions

About the team

The AR Data Engineering (ARDE) team develops and maintains robust data solutions that power Global Accounts Receivable operations. We build comprehensive datasets for collections, cash management, customer contacts, and billing processes that drive critical business insights. Our scalable platform transforms how AR teams operate by providing near-time visibility into cash flow and collections, optimizing working capital management, enhancing customer experience, and enabling secure, efficient data access and analysis. Through our sophisticated data infrastructure, we ensure AR teams can easily discover, access, and analyze the right information at the right time, empowering better decision-making across the organization."

BASIC QUALIFICATIONS :

- 1+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)

PREFERRED QUALIFICATIONS :

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.

📌 Data Engineer, Finauto, Accounts Receivable Data Engineering (Hyderabad)
🏢 ADCI HYD 13 SEZ
📍 Hyderabad