About the role
Job Family: Data Engineering & Architecture Consulting Travel Required: Up to 10% Clearance Required: Ability to Obtain Public Trust What You Will Do: Design, build, and optimize scalable, production-grade data ingestion, transformation, and analytics-ready pipelines using Databricks (Delta Lake, Delta Live Tables, Auto Loader) and AWS services, enabling trusted, timely data access across enterprise use cases. Engineer standardized, repeatable data pipelines supporting batch and near real-time processing, integrating legacy data sources and modern cloud-native services to advance enterprise data availability and eliminate data access gaps. Execute full delivery lifecycle by supporting intake, discovery, source profiling, and technical design, while maintaining
requirements traceability and aligning solutions to Architecture Review Board (ARB) and governance expectations. Implement and maintain governed data pipelines with embedded metadata, lineage, and data quality controls, ensuring pipelines meet defined technical, security, and documentation
requirements before production deployment. Develop and operationalize data engineering frameworks that incorporate observability, monitoring, alerting, and resilient error handling to maintain platform stability and support ≥99.9% availability targets. Partner with platform engineering and cloud operations teams to integrate pipelines with AWS services (S3, Glue, Kafka/Kinesis, APIs), enabling secure, scalable data movement and cross-platform interoperability. Enable governed analytics and self-service data consumption through curated datasets, semantic layers, and SQL warehouse integration, supporting enterprise reporting, dashboards, and advanced analytics use cases. Apply security and compliance controls aligned to IRS cybersecurity policies, including RBAC/ABAC, data masking, encryption, and audit logging to protect sensitive data and maintain regulatory compliance. What You Will Need: Bachelor’s degree is required Minimum EIGHT (8) years of experience in data engineering within cloud-based environments, Minimum FOUR (4) years of hands-on Databricks experience designing scalable data pipelines. Advanced proficiency in Python, PySpark, and SQL for large-scale data processing and pipeline development in lakehouse architectures. Demonstrated experience building and optimizing data pipelines leveraging Delta Lake, medallion architecture, and modern data ingestion frameworks (batch and streaming). Strong experience with AWS data platforms and services (e.g., S3, IAM, VPC, Glue, streaming frameworks) and integration with enterprise data ecosystems. Experience delivering production-ready data solutions incorporating metadata management, lineage, data quality, and observability frameworks. Familiarity with DevSecOps and CI/CD pipeline implementation, including automation, testing, and deployment within cloud data environments. Knowledge of data governance, security, and compliance