Data Development Lead

Data Strategy and Management

• Develop and implement the organization’s enterprise data strategy aligned with business and technology objectives.
• Establish and maintain data governance standards, policies, and operating models across the data lifecycle.
• Ensure enterprise data is accurate, secure, accessible, and reliable for business use.
• Promote a data-driven culture by improving access to trusted data, analytics, reports, and AI-enabled insights.

Data Warehouse and Engineering

• Lead the architecture, development, and continuous improvement of the enterprise data warehouse using Microsoft Fabric.
• Oversee the development of scalable and automated ETL/ELT pipelines.
• Manage data integration across enterprise systems, cloud platforms, operational systems, and external data sources.
• Establish standards for data modeling, transformation, testing, deployment, documentation, and performance monitoring.
• Drive the modernization of legacy data platforms and manual processes.

API Management and Systems Integration

• Lead the organization’s API strategy, architecture, governance, and delivery using MuleSoft.
• Oversee the design, development, deployment, and management of secure and reusable APIs.
• Promote API-led connectivity and seamless integration across enterprise systems and third-party services.
• Establish standards for API security, versioning, documentation, monitoring, and lifecycle management.

Application Development and AI

• Lead the planning, design, development, implementation, and support of enterprise applications.
• Oversee the delivery of web, mobile, workflow, automation, integration, and AI-enabled solutions.
• Lead the development and implementation of Agentic AI solutions, intelligent automation, and AI-assisted workflows.
• Identify business opportunities where AI agents and emerging technologies can create measurable business value.
• Establish appropriate controls for responsible AI, including security, data privacy, human oversight, and risk management.

Technology Leadership and Governance

• Define and maintain the target architecture for data platforms, APIs, applications, and AI solutions.
• Ensure alignment with enterprise architecture, cloud strategy, cybersecurity standards, and technology roadmaps.
• Review solution designs and promote scalable, modular, secure, and reusable technology solutions.
• Evaluate emerging technologies based on business value, scalability, security, and total cost of ownership.

Project and Team Management

• Manage the data, integration, application development, and AI project portfolio.
• Lead multidisciplinary teams across data engineering, API development, software engineering, DevOps, QA, AI engineering, and solution architecture.
• Manage project priorities, resources, budgets, timelines, risks, and external technology partners.
• Coach and develop team members while fostering a culture of innovation, accountability, collaboration, and continuous improvement.

Job Qualifications:

• Bachelor’s degree in Information Technology, Computer Science, Computer Engineering, Data Science, Software Engineering, or a related field.
• At least 10 years of progressive experience in information technology, data management, data engineering, systems integration, or application development.
• At least 5 years of experience in a leadership or management role.
• Proven experience in managing enterprise data warehouses, ETL/ELT platforms, APIs, systems integration, and application development teams.
• Experience in delivering enterprise-scale digital transformation, data modernization, or AI initiatives.
• Experience working with cloud-based platforms, Agile methodologies, DevSecOps, and enterprise architecture.
• Experience in managing vendors, technology partners, project budgets, and cross-functional teams.
• Strong knowledge of Microsoft Fabric.
• Strong understanding of data warehouses, data lakes, lakehouses, and data marts.
• Knowledge and experience in ETL/ELT architecture and development.
• Strong knowledge of data modeling, dimensional modeling, and semantic models.
• Knowledge of data quality, metadata management, data lineage, and master data management.
• Experience with MuleSoft Anypoint Platform.
• Knowledge of API design, API-led connectivity, integration patterns, and API security.
• Knowledge of cloud architecture and cloud-native application development.
• Experience in web and mobile application development.
• Knowledge of microservices and event-driven architecture.
• Experience with DevSecOps, CI/CD, source code management, and automated testing.
• Knowledge of Artificial Intelligence, Generative AI, Large Language Models (LLMs), and Agentic AI.
• Understanding of Retrieval-Augmented Generation (RAG) and enterprise knowledge systems.
• Knowledge of AI orchestration frameworks, evaluation, monitoring, guardrails, and responsible AI.
• Knowledge of Agile, Scrum, product management, and project portfolio management.