Manager Data Engineering

• Plan, design, and build multiple modern data products simultaneously, including data pipelines, data streams, data services, analytical data platforms, and AI-ready data products.

• Lead the development of scalable data foundations that support reporting, advanced analytics, machine learning, generative AI, automation, and real-time decision-making.

• Guide the implementation of machine learning and AI solutions, including data preparation, feature engineering, model development, deployment, monitoring, and lifecycle management.

• Support the development and productionization of AI/ML use cases using platforms such as Azure Machine Learning, Databricks, and other cloud-based technologies.

• Enable generative AI solutions, including large language model applications, retrieval-augmented generation, prompt engineering, vector search, AI agents, and enterprise knowledge solutions where appropriate.

• Partner with data scientists, AI engineers, product owners, and business stakeholders to translate business problems into practical and measurable AI solutions.

• Establish reusable patterns for model serving, AI application integration, APIs, batch and real-time inference, and integration with enterprise systems.

• Ensure data pipelines and platforms provide trusted, well-governed, secure, high-quality, and appropriately documented data for AI and analytics use cases.

• Define and implement data and AI architectures that support scalability, resilience, performance, cost optimization, and maintainability.

• Manage cross-team dependencies and work with technical leads and architects to develop effective solutions.

• Apply software engineering practices to data and AI products, including version control, automated testing, CI/CD, infrastructure as code, observability, and production support.