Software Engineer III - Data Engineer

As a Senior Associate Data Engineer within the Corporate Technology Risk organization, you will contribute to the development and modernization of the Consumer and Community Banking Risk Feature Engineering Platform. You will be expected to apply strong software engineering and data engineering practices while contributing to the platform's strategic direction through technical execution, innovation, automation, and continuous improvement.

Job Responsibilities

• Design, develop, and support scalable feature engineering solutions on Databricks that enable risk analytics, fraud detection, machine learning, and enterprise data products.

• Build and maintain reusable batch and real-time feature pipelines, including feature onboarding, versioning, testing, monitoring, and lifecycle management within the Risk Feature Store ecosystem.

• Implement modern data engineering solutions using Databricks, Apache Spark, PySpark, Delta Lake, Lakeflow, and declarative pipeline patterns, ensuring scalability, resiliency, and maintainability.

• Drive platform modernization initiatives by migrating legacy Spark and EMR workloads to Databricks-native architectures and adopting cloud-native engineering practices.

• Apply software engineering best practices including CI/CD, automated testing, code reviews, observability, release management, and production support to deliver high-quality, reliable solutions.

• Leverage enterprise-approved AI-assisted engineering tools such as GitHub Copilot and LLM Suite to accelerate development, improve code quality, automate SDLC activities, and identify opportunities for innovation.

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Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

• Implement data quality, governance, lineage, and security controls, ensuring compliance with regulatory requirements, PCI standards, metadata management, retention policies, and audit expectations.

• Develop and support cloud-native solutions on AWS, utilizing services such as S3, Glue, Lambda, ECS/EKS, Aurora/RDS, and Infrastructure-as-Code technologies including Terraform.

• Participate in architecture reviews and technical decision-making, contributing recommendations that improve platform performance, operational stability, cost efficiency, resiliency, and long-term scalability.

• Collaborate with data scientists, model developers, business stakeholders, and engineering teams, while mentoring junior engineers and promoting reuse-first, secure, and high-performing engineering practices across the organization.