Data Engineer

AI and Data EngineerUse GitHub Copilot as a hands-on engineering environment to analyze existing code, design and implement production-quality changes, generate and maintain tests and documentation, debug failures, review proposed changes, and verify outcomes with executable evidence. Design, build, test, and operate AI agents that plan and execute multi-step workflows using LLM reasoning, structured prompts, context and memory, retrieval, APIs, data tools, and human approval gates to enhance data engineering, reporting, testing, and operational processes. Develop tool-enabled agents and Model Context Protocol (MCP) integrations that securely connect AI assistants to enterprise APIs and platforms, including BI/reporting services, data products, work-management systems, and operational knowledge sources. Implement browser and UI automation agents for authenticated enterprise workflows, evidence capture, regression testing, test generation and healing, and operational workflows, using tools such as Playwright and browser developer protocols. Define agent evaluation, observability, and safety controls, including grounded-response checks, deterministic tool contracts, structured outputs, least-privilege credential handling, approval checkpoints, test datasets, execution traces, failure recovery, and measurable quality and productivity outcomes. Develop and implement data mesh and data fabric architectures to enable decentralized data management and access. Build data and BI development agents that generate and validate SQL, DAX, semantic models, reports, data-product specifications, quality tests, and deployment artifacts while preserving governance, auditability, and human review. Develop user personas and business personas in alignment with data requirements and deliver solutions that meet business needs. Work with business users to translate functional specifications into technical designs for implementation and deployment. Work with cross functional team members to develop prototype, produce design artifacts, develop components, perform and support SIT and UAT testing, triaging and bug fixing. Provide problem-solving expertise and complex analysis of data to develop business intelligence integration designs. Ensure high quality and optimum performance of data systems to meet business expectations.2+ years of hands-on experience using GitHub Copilot or similar AI-assisted engineering tools across the software development lifecycle, including requirements analysis, coding, refactoring, testing, debugging, documentation, code review, and verification. 2+ years of hands-on experience designing and building Agentic AI solutions using prompt and context engineering, LLMs, RAG, structured tool/function calling, APIs, memory or state management, human-in-the-loop controls, and AI governance principles. Hands-on programming experience in Python, Java, TypeScripts and SQL, with the ability to design agent tools, API clients, MCP servers, command-line workflows, typed data contracts, and automated tests for enterprise use cases. Experience integrating agents with enterprise authentication, APIs, databases, knowledge repositories, BI platforms, and browser automation while protecting credentials, sensitive data, and audit trails. Demonstrated ability to evaluate agent quality through unit, integration, behavioral, and end-to-end tests; diagnose hallucinations and tool failures; instrument execution; and improve prompts, retrieval, context, and workflows using evidence.Opportunity to work with a dynamic team in a fast-paced environment. Exposure to cutting-edge technologies and methodologies. Supportive and collaborative work culture.