AI AXISS INC
Senior AWS Data Engineer Cloud AWS
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior AWS Data Engineer with 10+ years of experience, located in Wilmington, DE. Key skills include AWS Glue, PySpark, and ETL. Experience in banking and financial services is required. Contract length and pay rate are unspecified.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
February 10, 2026
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
New Jersey, United States
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🧠 - Skills detailed
#Data Catalog #Compliance #AWS (Amazon Web Services) #Data Engineering #Hadoop #Lambda (AWS Lambda) #"ETL (Extract #Transform #Load)" #AWS Glue #Metadata #Data Quality #Batch #S3 (Amazon Simple Storage Service) #Cloud #Redshift #Data Lake #Spark (Apache Spark) #Data Ingestion #Data Governance #PySpark
Role description
Experience : 10+ years
Location: IBM/Barclays - Wilmington, DE
Designed and implemented enterprise-scale data lake architecture on AWS, ingesting high-volume banking and card transaction data into Amazon S3 using AWS Glue and PySpark.
Built and maintained serverless ETL pipelines using AWS Glue, reducing batch processing time
Developed PySpark transformations on AWS Glue and EMR to process terabytes of transactional and customer data daily.
Implemented data ingestion frameworks from multiple banking systems using Glue, Lambda, and Step Functions.
Optimized Spark jobs and partitioning strategies, improving query performance and reducing compute costs by 30%+.
Created data catalog and metadata governance using AWS Glue Data Catalog for improved discoverability and lineage.
Automated data validation and reconciliation checks to ensure data quality and compliance for financial reporting.
AWS Glue, PySpark, Hadoop, Spark, EMR, S3, Redshift, Banking, US Cards, Transaction Data, Fraud Analytics, Risk Analytics, Data Lake, ETL, Data Engineering, Financial Services
Experience : 10+ years
Location: IBM/Barclays - Wilmington, DE
Designed and implemented enterprise-scale data lake architecture on AWS, ingesting high-volume banking and card transaction data into Amazon S3 using AWS Glue and PySpark.
Built and maintained serverless ETL pipelines using AWS Glue, reducing batch processing time
Developed PySpark transformations on AWS Glue and EMR to process terabytes of transactional and customer data daily.
Implemented data ingestion frameworks from multiple banking systems using Glue, Lambda, and Step Functions.
Optimized Spark jobs and partitioning strategies, improving query performance and reducing compute costs by 30%+.
Created data catalog and metadata governance using AWS Glue Data Catalog for improved discoverability and lineage.
Automated data validation and reconciliation checks to ensure data quality and compliance for financial reporting.
AWS Glue, PySpark, Hadoop, Spark, EMR, S3, Redshift, Banking, US Cards, Transaction Data, Fraud Analytics, Risk Analytics, Data Lake, ETL, Data Engineering, Financial Services
