Description

· 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 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.
· 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 enable 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 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.
Job Requirements:
Bachelors’ Degree (or foreign equivalent degree) in Information Technology, Information Systems, Computer Science, Software Engineering, or a related field. Experience in the financial services or banking industry is preferred.
· 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, container, event-driven, security, and CI/CD patterns.
· 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.
Broader candidate background preferences -
· 5+ years of experience with data virtualization, data mesh, data fabric, and federated querying platform such as Denodo, Starburst or OSS platforms is highly desirable.
· 5+ Years of experience working as a Report Visualization Engineer with Power BI, Tableau, or any similar Reporting Platforms with End-to-End delivery.
· 3+ Year of experience with implementation of Data Modelling, Data Governance and RLS.
· 3+ Years of experience with Enterprise Deployment Strategies and migration of legacy platform reports to modern reporting platforms.
· Extract, transform, and load large volumes of structured and unstructured data from various sources into AWS data lakes or modern data platforms like Snowflake.
· Solid understanding of data modeling, database design, and ETL principles.
· Familiarity with data governance, data security, and compliance practices in cloud environments.
· Strong problem-solving skills and the ability to optimize and fine-tune data pipelines and Spark jobs for performance.
· Tableau/Power BI / Snowflake / Starburst certifications on Data related specailities are a plus.
· Power Platform experience (Power Apps, Power Automate) will be a plus.
Skillset Rubric -
Business Acumen – 15%: Knowledge of Banking & Financial Services Products (such as Loans, Deposits, Forex, etc.). Knowledge of Operational/MIS Reports, Risk and Regulatory Reporting for a Client is a plus.
Data Skills – 25%: Must have proficiency in Data Warehousing concepts, Data Lake & Data Mesh concepts, Data Modeling, Databases, Data Governance, Data Security/Protection, and Data Access.
Tech Skills – 50%: Hands-on data and software engineering using Python, Java, TypeScripts, SQL, AWS, Snowflake, Starburst, BI platforms, GitHub Copilot, LLM/RAG and agent frameworks, MCP/API integrations, Playwright/browser automation, automated testing, observability, and secure enterprise deployment practices.
Human Skills – 10%: Excellent communication and collaboration skills, with the ability to work effectively in a team environment

Key Skills

Education

Bachelor’s degree