Build secure, reliable backend services and APIs that enable self-service workflows (e.g., onboarding, workspace/environment provisioning, policy/guardrails, job orchestration hooks, environment configuration, secrets/access patterns).
Implement infrastructure-as-code and automation for AWS platform components (identity controls, encryption, networking integrations, logging/monitoring, guardrails).
Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contribute learnings and reusable patterns to improve broader team effectiveness.
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.
Produce architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development (security, resiliency, scalability, cost).
Drive reliability and observability practices (logs/metrics/tracing, retries, idempotency, timeouts, runbooks) and continuously improve platform operations.
Partner with IAM/security, data engineering, and application teams to integrate platform controls and enable secure analytics at scale.
Proactively identify hidden problems and patterns in operational data and use these insights to drive improvements to coding hygiene and system architecture.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability.
Strong experience developing, debugging, and maintaining production code in a large corporate environment with Python or another modern programming language.
Familiarity with modern front-end technologies such as React, TypeScript, HTML/CSS.
Strong experience with AWS and infrastructure fundamentals, including services such as IAM, KMS, S3, CloudWatch (and compute/orchestration such as EKS/ECS/Lambda where applicable).
Hands-on experience with infrastructure-as-code and automation-first engineering.
Solid understanding of the SDLC, including CI/CD, automated testing, and safe deployment practices.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Preferred qualifications
Hands-on experience with Databricks on AWS or similar data engineering / analytics platforms (e.g., Spark-based environments, data lake/lakehouse platforms) and ecosystem components (jobs/orchestration, cluster/runtime concepts, governance/catalog concepts).
Experience with streaming or distributed systems (e.g., Apache Kafka) in production environments (good to have).
IAM domain knowledge (Auth, authorization patterns, RBAC/ABAC concepts).
Experience with observability tooling (Datadog, Splunk, Dynatrace, Grafana).
