Data Engineer - FinTech, Fintech
Amazon
DESCRIPTION
We are seeking a highly skilled Data Engineer to join our FinTech ADA team, responsible for building and optimizing scalable data pipelines and platforms that power analytics, automation, and decision-making across Finance and Accounting domains. The ideal candidate will have strong expertise in AWS cloud technologies including Redshift, S3, AWS Glue, EMR, Kinesis, Firehose, Lambda, and IAM, along with hands-on experience designing secure, efficient, and resilient data architectures.
You will work with large-scale structured and unstructured datasets, leveraging both relational and non-relational data stores (object storage, key-value/document databases, graph, and column-family stores) to deliver reliable, high-performance data solutions. This role requires strong problem-solving skills, attention to detail, and the ability to collaborate with cross-functional teams to translate business needs into technical data solutions.
Key job responsibilities
Scope -
Fintech is seeking a Data Engineer to be part of Accounting and Data Analytics team. Our team builds and maintains data platform for sourcing, merging and transforming financial datasets to extract business insights, improve controllership and support financial month-end close periods. As a contributor to a crucial project, you will focus on building scalable data pipelines, optimizations of existing pipelines and operation excellence.
Qualifications-
• 5+ yrs experience as Data Engineer or in a similar role
• Experience with data modeling, data warehousing, and building ETL pipelines
• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.
• Extensive experience working with AWS with a strong understanding of Redshift, EMR, Athena, Aurora, DynamoDB, Kinesis, Lambda, S3, EC2, etc.
• Experience with coding languages like Python/Java/Scala
• Experience in maintaining data warehouse systems and working on large scale data transformation using EMR, Hadoop, Hive, or other Big Data technologies
• Experience mentoring and managing other Data Engineers, ensuring data engineering best practices are being followed
• Experience with hardware provisioning, forecasting hardware usage, and managing to a budget.
• Exposure to large databases, BI applications, data quality and performance tuning