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Key Responsibilities:

• Design, build, and maintain ETL/ELT pipelines using AWSGlue, Lambda, and Step Functions to ingest, transform, and load data acrossAWS-based data platforms (S3, Redshift, Athena)

• Maintain, enhance, and troubleshoot existing SQLServer-based ETL processes built in SSIS, including packages, control flows,data flows, and error handling

• Write and optimize complex T-SQL queries, storedprocedures, and views supporting reporting and downstream applications

• Support ongoing migration and modernization of legacySQL Server/SSIS ETL workloads to AWS-native data pipelines

• Design and maintain data models (dimensional/starschema) supporting analytics and reporting use cases

• Use AWS Database Migration Service (DMS) and relatedtools to support data migration from on-premises SQL Server to AWS

• Implement data quality checks, validation, andmonitoring across ETL pipelines

• Optimize pipeline performance, reliability, and costacross both AWS-native and SQL Server/SSIS workloads

• Partner with BI, integration, and application teams(including the Power BI and Edifecs/EDI teams within this applicationlandscape) to ensure data availability and consistency across systems

• Document data flows, pipeline architecture, and datamodels to support internal knowledge sharing

• Troubleshoot and resolve production data pipelineissues, including root-cause analysis

• Mentor junior engineers on data engineering and ETL/ELTbest practices

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Required Qualifications:

• Bachelor's degree in Computer Science, Engineering,Information Systems, or a related field

• 8–12 years of experience in data engineering or ETLdevelopment

• Strong, hands-on experience with AWS data services: S3,Redshift, Glue, Athena, Lambda, and Step Functions

• Strong, hands-on experience with SQL Server, includingT-SQL development, query optimization, and performance tuning

• Strong, hands-on experience building and maintainingETL packages in SSIS (control flow, data flow, error handling, and deployment)

• Working proficiency in Python for scripting,automation, and Glue/PySpark-based ETL development

• Solid understanding of dimensional data modeling (starschema) for analytics and reporting use cases

• Experience with data migration tools and approaches(e.g., AWS DMS) for moving workloads from on-premises SQL Server to AWS

• Strong understanding of data quality, governance, andlineage practices

• Strong debugging, performance-tuning, andproduction-support skills across ETL pipelines

• Excellent written and verbal communication skills forcross-functional collaboration

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AI Knowledge & AI-Assisted Development (Required):

• Daily, practical use of AI coding/assistant tools(e.g., Claude Code, GitHub Copilot, Cursor, or similar) to acceleratedevelopment of Glue/PySpark scripts, SSIS package logic, and T-SQL queries

• Able to critically review and validate AI-generatedcode, queries, and transformations for correctness, performance, and dataintegrity before deployment

• Practical use of AI tools to assist with dataprofiling, anomaly detection, and technical documentation

• Understanding of secure and compliant AI tool usage,including never entering PHI, member data, or other sensitive information intoprompts or external AI tools

• Able to identify where AI-driven automation can improvepipeline development, testing, or migration efficiency, and champion adoptionwithin the team

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Preferred Qualifications:

• Experience in the US health insurance or payer domain:claims, eligibility, enrollment, provider, or member data, with HIPAA-awaredata handling practices

• Exposure to healthcare data standards (X12 EDI, HL7,FHIR)

• Experience with Power BI or other BI/reporting toolsconsuming the data pipelines you build

• Experience with additional AWS data services: EMR,Kinesis, or Redshift Spectrum

• Experience with modern orchestration tools (e.g.,Apache Airflow) as an alternative or complement to SSIS

• Relevant certifications: AWS Certified DataEngineer/Analytics Specialty, Microsoft SQL Server certifications