AVP Data Engineering Lead

Architect, build, and optimize scalable data pipelines using Python, Spark, and Databricks for batch, streaming, and real-time workloads.

Design, develop, and maintain ELT/ETL pipelines, data transformations, and feature engineering workflows.

Design, prototype, test, and productionize AI and Generative AI solutions, moving beyond PoCs to enterprise-ready deployments.

Build and deploy cloud-native data and AI solutions on Microsoft Azure, leveraging services such as Azure OpenAI, Azure Databricks, AKS, App Services, Azure Functions, and Azure Storage.

Develop containerized data and AI services using Docker and Kubernetes, following DevOps and CI/CD best practices.

Design and implement secure APIs and microservices to expose data and AI capabilities to downstream applications.

Implement monitoring and observability for data pipelines and AI systems, including latency, data quality, model performance, drift, and cost metrics.

Optimize cloud and AI workloads for performance, scalability, and cost efficiency.

Actively contribute code, perform design and code reviews, and troubleshoot complex production issues across data and AI systems.

Serve as a hands-on technical mentor, leading by example within data engineering and AI teams.

Influence technical standards and long-term architecture while remaining deeply involved in day-to-day delivery.