Senior Data Engineer, DaS FinTech

Are you a highly skilled Senior data engineer and project leader? Do you think big, enjoy complexity and building solutions that scale? Are you curious to know what you could achieve in a company that pushes the boundaries of modern technology? If you answered yes and you have a background in FinTech you’ll love this role and Amazon’s data obsessed culture.

Amazon Devices and Services Fintech is the global team that designs and builds the financial planning and analysis tools for wide variety of Amazon’s new and established organizations. From Kindle to Ring and even new and exciting companies like Kuiper (our new interstellar satellite play) this team enjoys a wide variety of complex and interesting problem spaces. They are almost like FinTech consultants embedded in Amazon.

We are looking for an experienced Senior Data Engineer who has a passion for innovation and invention, working backwards from the customer, and tackling complex ambiguous problems. They are an influential leader and builder; a technical expert who can collaborate effectively across product and business, and are able to attract, engage and retain diverse talent, including other data engineering leaders. Data Engineer brings deep expertise in big data technologies, strong data modeling and problem-solving skills, and a track record of delivering scalable, maintainable data systems.

We develop data pipelines using Spark/Scala EMR, SQL-based ETL, and Airflow. We are looking for a talented, enthusiastic, and detail-oriented Data Engineer who knows how to take on big data challenges in an agile way. If you’re excited by the challenge of shaping data platforms that support real-world hardware, software, and operations, this is the role for you.

Key job responsibilities
- Develop and manage scalable, automated, and fault-tolerant data solutions using technologies such as Spark, EMR, Python, Redshift, Glue, and S3.
- Architect and implement scalable, reliable, and secure data pipelines and infrastructure to support analytics, reporting, and business operations.
- Design and enforce data modeling standards, data lineage, and governance frameworks that ensure consistency, reusability, and quality across systems.
- Lead design and code reviews, driving engineering best practices across data development, documentation, testing, and monitoring.
- Build data platforms and frameworks that support both batch and real-time processing, enabling flexible, efficient analytics at scale.
- Define data models and schemas optimized for both operational reporting and statistical/econometric model consumption
- Build automated data quality frameworks that ensure accuracy and reliability for high-stakes business decisions
- Engineer self-service data access through metadata-rich catalogs, governed query layers, and dashboard-ready datasets that enable stakeholders to answer recurring questions
- Build the measurement infrastructure for business experiments (A/B tests, weblabs), ensuring clean experiment data and statistically valid result datasets
- Drive cost optimization and data governance across the analytics data estate: lineage tracking, metric definitions, access controls, and SLA definitions

Basic Qualifications

- 5+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience mentoring team members on best practices

Preferred Qualifications

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience operating large data warehouses
- Advanced degree (Master's) in Computer Science, Computer Engineering, or a quantitative field preferred
- Experience in leading and architecting Data solutions using AWS data services (Redshift, S3, Glue, EMR, Kinesis, Lambda, RDS, Bedrock) and understanding of IAM security frameworks.
- Experience communicating with users, other technical teams, and management to collect requirements, describe data modeling decisions and data engineering strategy
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering

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