We are looking for a versatile Software Engineer to join our Data Innovation Team. In this role, you won't just be managing data; you will be building the "brain" of our organization. You will own the entire product lifecycle—architecting high-performance data pipelines, implementing AI-driven logic, and developing the full-stack applications that bring these insights to life. If you are a builder who thrives at the intersection of Big Data and system engineering, we want you to help us turn complex data signals into our most competitive assets.

Key Responsibilities

• Solution Architecture: Translate high-level business requirements into technical specifications, utilizing Lakehouse architecture to ensure scalability and performance.
• End-to-End Software Development: Build and maintain full-stack applications, ensuring seamless integration between backends and frontend components.
• Frontend Development: Design and develop responsive, intuitive web interfaces and data visualizations that translate complex analytics into actionable user experiences.
• Data & AI Pipelines: Build and optimize declarative pipelines using Delta Live Tables (DLT) and orchestrate complex workflows to serve Machine Learning models.
• Operational Excellence: Manage the full application lifecycle, including governance via Unity Catalog, model tracking via MLflow, and CI/CD for both code and data.
• Cross-Functional Collaboration: Work closely with stakeholders to ensure successful project delivery, focusing on code modularity and maintainability within the Databricks ecosystem.

Technical Requirements

The Essentials:

• Education: Degree in Computer Science, Software Engineering, or a related field.
• Software Engineering: Deep mastery of Python (PySpark), Java, or C++. You should understand data-oriented design as well as you understand OOP.
• Full-Stack Mindset: Comfort moving from backend data logic to frontend API consumption.

The "Lakehouse" Stack (Highly Desired):

• Databricks Ecosystem: Hands-on experience with Delta Lake, Spark SQL, and Databricks Workflows.
• AI Operations: Experience using MLflow or Mosaic AI to deploy and monitor models in production.
• DevOps for Data: Familiarity with Databricks Asset Bundles (DABs), Git integration, and containerization (Docker).