• Build and ship product features end-to-end, contributing production code across our stack. • Execute on challenging technical problems while staying grounded in why the work exists, not just how to implement a solution. • Work closely with teammates across engineering, product, and design. • Contribute to your team’s culture by communicating openly, being receptive to feedback, and prioritizing shared outcomes. • Uphold a high bar for code quality, reliability, and maintainability, while avoiding over-engineering. • Make thoughtful day-to-day tradeoffs between velocity, quality, and scope, learning how to deliver impact quickly without sacrificing correctness. • Use modern tools (including AI) as leverage to explore solutions faster and deliver impact sooner. • Bring customer obsession to your development process, regularly asking: How does this make the experience better for our users?

• Candidates will build/maintain data migration and conversion tools, test scripts. • The successful candidate will be able to perform system integration of data migration solutions for our customers and also have the ability to work with customers to fulfill the business objectives and end-user expectations.

• System Architecture & C# Core Design and build the core automation engine using C# / .NET. • Ensure the code is clean, scalable, and follows modern design patterns (Solid, Clean Architecture). • Solve complex technical challenges, such as compiling code at runtime or handling Windows UI interactions. • AI Integration (Real-world Application) Integrate LLMs (OpenAI, Azure, etc.) into the software workflow. • Design the logic for how the system interacts with AI—focusing on prompt structure, context management, and error handling (Self-healing). • Bridge the gap between "Generative AI" and "Reliable Software Execution." • Leadership & Communication Mentor & Guide: Help other engineers grow. Conduct code reviews and set engineering standards. • Bridge the Gap: Translate technical architecture into clear explanations for PMs and stakeholders. • Collaborate: Work with a mindset of "we" not "I". You are a partner in product decisions.

• Design and implement semantically consistent, scalable 360 data models that integrate data across domains. • Build and maintain transformation pipelines that apply cleansing, standardization, enrichment, and derived logic to domain datasets. • Write production-quality, testable code in SQL and Python (or equivalent)—delivering performant and maintainable data assets. • Work closely with domain experts, data scientists, and product stakeholders to translate business concepts into interpretable, decision-ready data models. • Implement logic for classifications, KPIs, scoring algorithms, and business rules, ensuring traceability and data lineage. • Help define and enforce standards for data modeling, documentation, and governance within the semantic layer. • Collaborate across teams to integrate with ingestion, MDM, and data product layers.