Data Engineer II, Infra-Finance Business intelligence & Transformations
Confirmed on the employer's own hiring board on Oct 20, 2026. First seen by Alion on Oct 13, 2026. Amazon scores B on the Alion truth index.
Do you have a desire to make a major contribution to the future in the rapid growth environment of Cloud Computing? Are you passionate about data and code? Does the prospect of dealing with massive volumes of data excite you? Do you want to build data engineering solutions that process millions of records?
Amazon Web Services is looking for a Data Engineer to help build a scalable, secured and robust data infrastructure that supports the AWS supply chain Finance (SCF) organization. You will be part of the SCF Analytics team working with other Data Engineers, Business Intelligence engineers, Product managers and partner with finance/business stakeholders to achieve our goals.
Key job responsibilities
- Design, implement, and support large-scale data pipelines and ETL processes using AWS technologies
- Lead and design complex data architectures, ensuring scalability, security, and alignment with business objectives.
- Build and maintain efficient, scalable data pipelines and curated datasets optimized for AI consumption and self service analytics.
- Identify and resolve system-wide data challenges, including performance bottlenecks and architectural deficiencies.
- Tune application and query performance using profiling tools and SQL.
- Analyze and solve problems at their root, stepping back to understand the broader context.
- Keep up to date with advances in data technologies and run pilots to design the data architecture to scale with the increased data volume using AWS.
- Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for datasets.
- Triage many possible courses of action in a high-ambiguity environment, making use of both quantitative analysis and business judgment.
- Recognize and adopt best practices: data integrity, test design, analysis, validation, and documentation.
- Ensure data solutions are auditable, accessible, and maintainable across the organization
- Collaborate with cross-functional teams to deliver innovative data solutions About the team IFBIT team vision is to simplify and automate processes, drive data-led decision making and provide meaningful insights for enhanced financial and business performance. We aim to cultivate a culture of innovation and collaboration, build and evangelize data analytics capabilities, develop scalable products that reduce manual efforts, improve efficiency and enhance controllership. By doing this, we contribute to improving AWS financial performance in a sustainable manner. Our focus on scalability, agility, and customer-centric products also aligns with the goal of maximizing long-term value for our customers and the company.
Basic qualifications
- 5+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- 5+ years of SQL experience
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience as a data engineer or related specialty (e.g., software engineer, business intelligence engineer, data scientist) with a track record of manipulating, processing, and extracting value from large datasets
- Bachelor's degree or foreign equivalent in computer science, engineering, analytics, mathematics, statistics, IT or equivalent
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)
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
- Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
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