Senior Data Engineer

Senior Data Engineer – AWS, SQL Server & SSISExperience: 12–15 yearsTech Stack: AWS (S3, Redshift, Glue, Athena, Lambda, Step Functions, DMS), SQL Server (T-SQL), SSIS, Python, Data Warehousing/ETL, AI-Assisted DevelopmentRole OverviewWe are looking for a Senior Data Engineer to design, build, and maintain data pipelines across our health plan technology platform, spanning AWS-native data services and existing SQL Server/SSIS ETL processes. This role sits alongside the other engineering teams in this application landscape (software engineering, Data/BI & Integration, DevOps, and Edifecs EDI), and carries dual responsibility: keeping current SQL Server/SSIS workloads reliable, and helping migrate and modernize them onto AWS-native pipelines.Key ResponsibilitiesDesign, build, and maintain ETL/ELT pipelines using AWS Glue, Lambda, and Step Functions to ingest, transform, and load data across AWS-based data platforms (S3, Redshift, Athena)Maintain, enhance, and troubleshoot existing SQL Server-based ETL processes built in SSIS, including packages, control flows, data flows, and error handlingWrite and optimize complex T-SQL queries, stored procedures, and views supporting reporting and downstream applicationsSupport ongoing migration and modernization of legacy SQL Server/SSIS ETL workloads to AWS-native data pipelinesDesign and maintain data models (dimensional/star schema) supporting analytics and reporting use casesUse AWS Database Migration Service (DMS) and related tools to support data migration from on-premises SQL Server to AWSImplement data quality checks, validation, and monitoring across ETL pipelinesOptimize pipeline performance, reliability, and cost across both AWS-native and SQL Server/SSIS workloadsPartner with BI, integration, and application teams (including the Power BI and Edifecs/EDI teams within this application landscape) to ensure data availability and consistency across systemsDocument data flows, pipeline architecture, and data models to support internal knowledge sharingTroubleshoot and resolve production data pipeline issues, including root-cause analysisMentor junior engineers on data engineering and ETL/ELT best practicesRequired QualificationsBachelor's degree in Computer Science, Engineering, Information Systems, or a related field12–15 years of experience in data engineering or ETL developmentStrong, hands-on experience with AWS data services: S3, Redshift, Glue, Athena, Lambda, and Step FunctionsStrong, hands-on experience with SQL Server, including T-SQL development, query optimization, and performance tuningStrong, hands-on experience building and maintaining ETL packages in SSIS (control flow, data flow, error handling, and deployment)Working proficiency in Python for scripting, automation, and Glue/PySpark-based ETL developmentSolid understanding of dimensional data modeling (star schema) for analytics and reporting use casesExperience with data migration tools and approaches (e.g., AWS DMS) for moving workloads from on-premises SQL Server to AWSStrong understanding of data quality, governance, and lineage practicesStrong debugging, performance-tuning, and production-support skills across ETL pipelinesExcellent written and verbal communication skills for cross-functional collaborationAI Knowledge & AI-Assisted Development (Required)Daily, practical use of AI coding/assistant tools (e.g., Claude Code, GitHub Copilot, Cursor, or similar) to accelerate development of Glue/PySpark scripts, SSIS package logic, and T-SQL queriesAble to critically review and validate AI-generated code, queries, and transformations for correctness, performance, and data integrity before deploymentPractical use of AI tools to assist with data profiling, anomaly detection, and technical documentationUnderstanding of secure and compliant AI tool usage, including never entering PHI, member data, or other sensitive information into prompts or external AI toolsAble to identify where AI-driven automation can improve pipeline development, testing, or migration efficiency, and champion adoption within the teamPreferred QualificationsExperience in the US health insurance or payer domain: claims, eligibility, enrollment, provider, or member data, with HIPAA-aware data handling practicesExposure to healthcare data standards (X12 EDI, HL7, FHIR)Experience with Power BI or other BI/reporting tools consuming the data pipelines you buildExperience with additional AWS data services: EMR, Kinesis, or Redshift SpectrumExperience with modern orchestration tools (e.g., Apache Airflow) as an alternative or complement to SSISRelevant certifications: AWS Certified Data Engineer/Analytics Specialty, Microsoft SQL Server certifications