Software Engineer III, Data Engineer

What you'll do

• Design and develop scalable feature engineering solutions on Databricks for risk analytics and fraud detection.
• Build and maintain reusable batch and real-time feature pipelines within the Risk Feature Store ecosystem.
• Implement modern data engineering solutions using Apache Spark, PySpark, Delta Lake, and declarative pipeline patterns.
• Migrate legacy Spark and EMR workloads to Databricks-native architectures and cloud-native practices.
• Apply software engineering best practices including CI/CD, automated testing, code reviews, and production support.
• Integrate AI-assisted engineering tools like GitHub Copilot to accelerate development and automate SDLC activities.
• Implement data quality, governance, lineage, and security controls to ensure regulatory compliance.
• Develop and support cloud-native solutions on AWS using services like S3, Glue, Lambda, and Terraform.

What we're looking for

• Formal training or certification in software engineering, computer science, data engineering, or a related discipline with 3+ years of applied industry experience.
• Strong hands-on experience with Databricks, Apache Spark, PySpark, Delta Lake, and modern Lakehouse architectures.
• Proficiency in Python and SQL with a strong understanding of distributed computing principles.
• Experience building and supporting large-scale batch and streaming data pipelines.
• Hands-on experience using enterprise-authorized AI-assisted software development tools while ensuring output correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling of inputs/outputs.
• Experience with AWS cloud services (S3, Glue, EMR, Lambda, ECS/EKS) and implementing data quality, governance, and lineage solutions.
• Professional certifications such as Databricks Data Engineer or AWS Solutions Architect (preferred); experience with Feature Engineering platforms (preferred).