Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. About the Role This project is suited for a Senior Python developer with deep functional testing experience, strong Linux and Docker skills, the ability to read code across multiple languages with the support of LLMs (e.g., C, Rust, Go) and translate requirements for migration tasks, and confidence using tools like Roo Code or Claude Code to accelerate iterative development. Key Responsibilities - Create functional black box tests for large codebases in various source languages - Create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms - Monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards - Leverage LLMs (Roo Code, Claude) to accelerate development cycles, automate repetitive tasks, and improve overall code quality

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. About the Role This project is suited for a Senior Python developer with deep functional testing experience, strong Linux and Docker skills, the ability to read code across multiple languages with the support of LLMs (e.g., C, Rust, Go) and translate requirements for migration tasks, and confidence using tools like Roo Code or Claude Code to accelerate iterative development. Key Responsibilities - Create functional black box tests for large codebases in various source languages - Create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms - Monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards - Leverage LLMs (Roo Code, Claude) to accelerate development cycles, automate repetitive tasks, and improve overall code quality

• Lead and manage data migration projects within the financial services sector, ensuring seamless transfer of data between systems. • Develop detailed project plans, timelines, and risk mitigation strategies related to data migration activities. • Collaborate closely with business analysts, data architects, IT teams, and stakeholders to understand data requirements and migration scope. • Oversee data validation, cleansing, and testing to ensure data integrity and accuracy post-migration. • Coordinate resources and manage dependencies to meet project deadlines and quality standards. • Ensure compliance with regulatory requirements and data governance policies throughout the migration process. • Provide regular status updates, reports, and documentation to senior management and stakeholders. • Drive continuous improvement initiatives to optimize data migration methodologies and best practices.

• Embed Data Strategy: Partner with vertical Product Managers and Engineering leads during the design phase of new features. You will ensure that every Product Requirement Document (PRD) includes a defined data strategy—specifying exactly how data is captured, stored, and validated. • Design Review: Act as a technical reviewer for major product initiatives, asking the critical questions: *“How does this change the data model? Does this create a duplicate field in core applications? How will we report on this?”* • Standardization: Establish the standards for how our Product team defines data requirements, ensuring consistency in how we track loan lifecycles, customer statuses, and financial transactions across different squads. • Data Modeling: Oversee the logical data model within our Core Applications and Salesforce. You will ensure that as we scale, our schema remains clean, normalized, and efficient, preventing "tech debt" in the form of messy or redundant data fields. • Lineage & Overrides: Own the logic for Data Lineage and Data Overrides. You will define the rules for which system is the "source of truth" when data conflicts occur and ensure we have an audit trail for manual overrides in core apps. • Governance at the Source: Implement "Governance by Design" by working with Engineering to build validation rules and constraints directly into the application layer, preventing bad data from ever entering our ecosystem. • The Analytics Bridge: Serve as the primary liaison between the Product/Engineering organization and the Analytics/Data Science teams. You will ensure that upstream application changes do not break downstream reporting or AI models. • Translation: Translate complex analytical requirements (e.g., specific features needed for a risk model) into actionable engineering tickets for the Core Application teams. • Data Availability: Ensure that the data generated by our products is accessible, documented, and structured in a way that allows the Analytics team to self-serve, reducing the dependency on ad-hoc engineering support.