AI and Data Engineer

Job Summary:
Seeking an AI and Data Engineer with hands-on experience using GitHub Copilot and engineering AI agents that combine Large Language Models (LLMs), prompt and context engineering, Retrieval-Augmented Generation (RAG), APIs, enterprise data sources, and governed tool execution. The role will focus on designing and building secure, observable, testable, and production-grade AI and data solutions that automate data, reporting, testing, and operational workflows. The engineer will work with technology and business leads to build and enhance enterprise data applications across on-premises and AWS cloud environments, leveraging modern data platforms such as Snowflake and Starburst.


Key Responsibilities:
• Use 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 execute multi-step workflows using LLM reasoning, structured prompts, context and memory, retrieval, APIs, data tools, and human approval gates.
• 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 support decentralized data management and access.
• Design, build, and deploy cloud-native AI, data, and analytics solutions on AWS using serverless, containerized, and event-driven architectures.
• Develop automated deployment pipelines and cloud integrations to support secure, scalable, and reliable delivery of AI agents, data products, and reporting solutions.
• Build data and BI development agents that generate and validate SQL, DAX, semantic models, reports, data-product specifications, quality tests, and deployment artifacts while maintaining governance, auditability, and human review.
• Develop user and business personas aligned 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.
• Collaborate with cross-functional teams to develop prototypes, produce design artifacts, develop components, and perform and support SIT and UAT testing, triage, and bug fixing.
• Provide problem-solving expertise and perform complex data analysis to develop business intelligence integration designs.
• Ensure high quality and optimum performance of data systems to meet business expectations.


Required Qualifications:
• Bachelor's degree or foreign equivalent in Information Technology, Information Systems, Computer Science, Software Engineering, or a related field.
• 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.
• 3+ years of experience developing and deploying cloud-native applications on AWS, including serverless, containerized, event-driven, security, and CI/CD patterns.
• Hands-on programming experience with Python, Java, TypeScript, 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 testing; diagnose hallucinations and tool failures; instrument execution; and improve prompts, retrieval, context, and workflows using evidence.
• 5+ years of experience with data virtualization, data mesh, data fabric, and federated query platforms such as Denodo, Starburst, or open-source platforms is highly desirable.
• 5+ years of experience working as a Report Visualization Engineer with Power BI, Tableau, or similar reporting platforms with end-to-end delivery experience.
• 3+ years of experience implementing data modeling, data governance, and row-level security (RLS).
• 3+ years of experience with enterprise deployment strategies and migration of legacy platform reports to modern reporting platforms.
• Experience extracting, transforming, and loading large volumes of structured and unstructured data from various sources into AWS data lakes or modern data platforms such as Snowflake.
• Solid understanding of data modeling, database design, data warehousing, data lakes, data mesh, and ETL principles.
• Familiarity with data governance, data security, data protection, and compliance practices in cloud environments.
• Strong problem-solving skills with the ability to optimize and fine-tune data pipelines and Spark jobs for performance.
• Excellent communication and collaboration skills with the ability to work effectively across cross-functional teams.


Preferred Qualifications:
• Experience in the financial services or banking industry.
• Knowledge of banking and financial services products such as loans, deposits, and foreign exchange.
• Knowledge of operational/MIS reporting, risk reporting, and regulatory reporting within banking environments.
• Experience with Power Platform, including Power Apps and Power Automate.
• Experience with modern AI agent frameworks, MCP/API integrations, Playwright/browser automation, automated testing, observability, and secure enterprise deployment practices.


Certifications:
• Tableau, Power BI, Snowflake, Starburst, or similar data-related certifications are a plus.