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Job Summary
The Data Platform Engineer will join an Enterprise Data Platform Services team responsible for building and operating self-service capabilities that enable data scientists and engineers to develop and deliver scalable, trustworthy data solutions. The role will help shape an enterprise data platform supporting the data value chain from ingestion and transformation through insight delivery, with a focus on data quality, observability, platform scalability, and AI readiness. The engineer will collaborate with product managers, data scientists, and engineering teams to transform strategic goals into reliable platform capabilities and contribute to an extensible data product development platform.

Key Responsibilities
• Collaborate with product managers and stakeholders to define, scope, and lead feature development aligned with evolving user needs and enterprise priorities.
• Develop and operate core data platform capabilities using Python, FastAPI, Azure Kubernetes Service (AKS), and Databricks.
• Write well-tested, maintainable code and promote unit and integration testing through automated validation pipelines.
• Provide coaching, feedback, and technical guidance to junior and mid-level engineers.
• Lead retrospectives and technical design discussions to identify improvements in delivery pipelines, team workflows, and system reliability.
• Partner with product managers to scope, estimate, and plan releases for mid- to large-scale initiatives while balancing technical constraints and business value.
• Collaborate across engineering teams to promote reusability and contribute to a modern and flexible data management platform.
• Evolve engineering best practices across the team and platform while promoting craftsmanship, experimentation, and operational excellence.
• Contribute to the evolution of the platform to support AI readiness, intelligent observability, and autonomous agents.

Required Qualifications
• 10+ years of professional software development experience, including ownership of production systems.
• Proficiency in Python and experience building APIs using FastAPI or comparable frameworks.
• Strong SQL skills and experience working with structured data.
• Experience working in cloud environments, particularly Microsoft Azure.
• Experience with Azure services such as Function Apps, Service Bus, and AKS.
• Experience with Databricks and PySpark for data-intensive applications.
• Experience with Domain-Driven Design and event-driven architecture.
• Experience with version control systems such as Git and SVN.
• Strong familiarity with automated testing frameworks such as Pytest and Unittest.
• Understanding of Agile methodologies such as Scrum and XP.
• Knowledge of RESTful API design and integration.
• Experience with performance tuning, debugging, and dependency management in production environments.
• Understanding of CI/CD pipelines and object-oriented design principles such as SOLID.

Preferred Qualifications
• Familiarity with front-end technologies such as Angular, React, and TypeScript.
• Exposure to platform engineering or self-service data tooling.
• Experience supporting AI-driven data applications or agentic automation frameworks.

Certifications
• Databricks Certified Data Engineer Professional preferred.
• Cloud provider certifications such as Azure Certified Solutions Architect preferred.