About the department :

The Enterprise Data, Governance & Integration Platforms Engineering team builds and manages foundational data, API, integration, and application platforms that enable scalable, secure, and reliable data, analytics, AI, and business solutions across the enterprise. The team develops cloud-native services, modern web applications, APIs, and reusable platform capabilities that accelerate data engineering, system integration, analytics, governance, and platform operations. Leveraging AWS, Azure, modern software engineering practices, CI/CD automation, and AI-assisted development, the team drives engineering productivity, platform adoption, and operational excellence. We partner closely with data, analytics, infrastructure, security, governance, and AI organizations to deliver enterprise-scale platform capabilities, hybrid cloud integrations, and enterprise-grade governance.

How you will add value :

- You will own the technical architecture and engineering direction for enterprise data platform frameworks, SDKs, and services.

- You will lead the design and delivery of reusable data engineering frameworks that accelerate ingestion, transformation, orchestration, and delivery across enterprise teams.

- You will architect scalable, event-driven platform components for batch and real-time data processing using AWS and Python.

- You will drive Data Lake and event-driven platform capabilities that enable self-service data engineering at enterprise scale.

- You will establish and enforce engineering standards for security, testing, observability, reliability, and governance across platform components.

- You will drive CI/CD automation through GitLab pipelines, Infrastructure as Code (Terraform), and deployment automation.

- You will mentor and develop senior engineers, fostering technical growth, architectural thinking, and platform engineering leadership.

- You will lead technical design reviews, architecture discussions, and cross-team technical alignment for data platform capabilities.

- You will champion AI-assisted development practices and Agentic AI workflows to improve platform engineering productivity and delivery velocity.

- You will design and refine system context, prompts, guardrails, and orchestration workflows that enable AI agents to execute complex engineering tasks reliably and securely.

- You will represent the data platform engineering team in leadership forums, steering committees, and architecture review boards.

- You will influence technical strategy and roadmap decisions across data engineering, analytics, and AI platform organizations.

What will help you be successful in this role :

Experience & Certifications :

- 9+ years of software engineering experience with progressive technical leadership responsibility in platform, framework, or infrastructure development.

- 3+ years building cloud-native solutions on AWS (Lambda, S3, Glue, AppSync, ECS/EKS, EventBridge, Kinesis, Step Functions, Airflow, DMS, Bedrock).

- Experience leading or mentoring engineering teams on data platforms or infrastructure projects.

- Proven experience designing and maintaining reusable frameworks, SDKs, or platform services adopted by multiple teams.

- AWS Solutions Architect or Data Analytics certification preferred.

- Bachelors degree in computer science, Engineering, or a related field (or equivalent practical experience).

Technical Skills :

- Strong Python development expertise includes object-oriented design, design patterns, and testing practices.

- Deep understanding of distributed systems, event-driven architectures, scalability, and performance optimization.

- Experience architecting Data Lake solutions, Apache Iceberg, or lake-house patterns at enterprise scale.

- Proficiency with CI/CD pipelines (GitLab preferred), Infrastructure as Code (Terraform), and deployment automation.

- Experience with containerization (Docker, Kubernetes) and microservices patterns.

- Hands-on experience with real-time streaming platforms (Kafka, Kinesis).

- Leverage Agentic AI development tools and coding agents to accelerate framework development, code generation, testing, and modernization.

- Design and refine system context, prompts, guardrails, and orchestration workflows that enable AI agents to execute complex engineering tasks reliably and securely.

- Integrate AI-enabled development practices into platform engineering, CI/CD pipelines, and developer workflows while maintaining enterprise security and quality standards.

- Drive adoption of AI-powered engineering capabilities that improve developer productivity and delivery velocity.

- Experience with observability platforms (CloudWatch, Datadog, OpenSearch).

- Java development experience as a secondary language.