Create and maintain CI/CD pipelines for infrastructure and platform deployments, including Terraform plan/apply workflows, policy checks, and automated testing
Implement security best practices across the platform, including least-privilege access controls, secrets management, encryption, network segmentation, and auditability
Improve platform reliability through observability tooling — logging, metrics, and tracing — alongside alerting, incident response practices, and performance and cost optimization
Collaborate with stakeholders to translate business and technical requirements into scalable platform designs, and document standards, patterns, and runbooks for team-wide use
Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards
Apply 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
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and proficient applied experience
Strong hands-on experience provisioning AWS infrastructure with Terraform, including modules, remote state, workspace/environment management, and change management workflows
Solid experience with core AWS services relevant to cloud data platforms, such as VPC, IAM, S3, EC2/ECS/EKS, RDS, KMS, CloudWatch, and load balancing
Working experience with Databricks, including platform administration and/or building and supporting production workloads
Proficiency in Python (preferred) or Java, with demonstrated ability to build automation, tooling, and platform integrations
Experience with Git-based workflows and CI/CD practices applied to infrastructure and platform delivery
Strong troubleshooting skills across cloud infrastructure, distributed systems, and data or platform pipelines
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations
Preferred qualifications, capabilities, and skills
Experience with data lakehouse architectures using Delta Lake, Apache Spark, or similar open-source frameworks
Familiarity with AWS-native data services such as Glue, Lake Formation, Redshift, or Athena
Exposure to data governance practices including cataloging, lineage tracking, and data access control at the platform layer
AWS certification(s) in cloud architecture, data, security, or DevOps disciplines
Experience working in regulated industries where security, auditability, and compliance are core platform requirements
