Software Engineer

Responsibilities

• Design, develop, and maintain web applications, APIs, and backend services using C#, .NET, Blazor, React, and TypeScript
• Build and extend solutions within the Microsoft Power Platform ecosystem, including Power Apps, Power Automate, and Dataverse
• Design and build scalable ETL/data pipelines in a medallion architecture using Azure Data Factory, Microsoft Fabric, Delta Lake, and Dataflow Gen2
• Develop and maintain dimensionally modeled data warehouses supporting reporting and analytics across legal, finance, and operational functions
• Integrate data from legacy SQL Server environments (SSRS, SSAS) and third-party systems using Azure Data Factory and Boomi
• Arcitect and build AI agents and RAG pipelines using Azure AI Foundry, Microsoft Agent Framework, and Copilot Studio — including document ingestion, vectorization, grounding, and prompt engineering
• Integrate AI capabilities into user workflows via Microsoft Copilot, Teams, and Office add-ins; define agent invocation patterns, context management, and enterprise governance models
• Implement observability, telemetry, and exception reporting across applications, data pipelines, and AI systems
• Document data models, lineage, transformation logic, ETL workflows, and AI architecture decisions to ensure traceability and maintainability
• Participate in sprint planning, architectural discussions, code reviews, and the firm's ongoing platform modernization strategy
• Use Git and Azure DevOps for version control, release management, and collaboration

Requirements

• Strong proficiency in C# and .NET for application and backend service development
• Hands-on experience with Blazor, React, and TypeScript for modern web development
• 5+ years of data engineering experience with Microsoft technologies; SQL Server and T-SQL expertise required
• Proficiency with ETL tools including Azure Data Factory, Synapse, and Boomi for third-party integration
• Proven experience building or operating AI-enabled systems in production, including RAG pipelines and agent frameworks
• Practical understanding of ML and generative AI concepts, including model limitations, responsible AI principles, and governance
• Familiarity with medallion architecture, dimensional modeling, and enterprise data warehouse design
• Understanding of data governance, security, and compliance in a legal context
• Experience with low-code/no-code platforms including Microsoft Power Platform
• Proficiency with Git and Azure DevOps
• Ability to explain technical decisions, AI behavior, and data architecture to non-technical stakeholders

Compensation

The national average salary range for this role is $72,000 - $121,000. Starting salary will be determined based on location, qualifications, and experience.