Senior Data Application Engineer

About the role

Cooley AI is the wholly-owned subsidiary of Cooley LLP, the frontier development arm of the leading technology law firm. Its mission is to bring best-in-class technology practices into the legal domain to help lead the firm and the legal industry into the AI era. As a Senior Data Application Engineer on the Innovation team, you will build and support interactive data applications on the firm’s Databricks platform, enabling users to access insights, workflows, and AI‑powered capabilities more efficiently.

Responsibilities

• Build and enhance interactive data applications using Databricks Appkit, AI‑assisted coding tools (e.g., Claude Code), and optional local development in VS Code, following GitHub‑based version control and pull‑request workflows.
• Implement UI components, data visualizations, and user‑facing features that integrate seamlessly with Databricks data sources and Lakehouse‑native APIs.
• Support the development of internal tools that streamline workflows, improve data accessibility, and enhance user experience across legal and business teams.
• Participate in code reviews, testing, and documentation to ensure application reliability, maintainability, and adherence to firm standards.
• Troubleshoot application issues, perform root‑cause analysis, and implement fixes under the guidance of senior engineers.
• Maintain and update content‑driven applications, ensuring accuracy, usability, and compliance with firm policies.
• Contribute to internal documentation, user guides, and knowledge bases for supported applications.
• Assist senior engineers in building and maintaining ingestion, transformation, and integration workflows on the Databricks platform.
• Write SQL queries to retrieve, validate, and prepare data for use in data apps.
• Monitor data jobs and application integrations, escalating issues when needed.
• Support data quality efforts through validation checks, structured testing, and documentation updates.
• Learn foundational concepts of Lakehouse architecture, Delta Lake, and Databricks‑native development practice.
• Work with Data Platform team members to understand data consumption patterns and ensure applications present information clearly and accurately.
• Collaborate with Product Owners to translate business requirements into actionable development tasks and user stories.
• Partner with Data Architects and platform teams to ensure applications align with governance, security, and architectural standards.
• Engage with Technology, Security, and