Provide technical leadership across Data Engineering, Cloud, AI, and enterprise data platform initiatives.
Lead, manage, mentor, and guide engineering teams to ensure high-quality delivery and continuous capability development.
Drive effective client engagement by understanding business priorities, managing expectations, communicating technical solutions clearly, and ensuring timely resolution of concerns.
Collaborate with cross-functional teams including business stakeholders, architects, product owners, engineering teams, governance teams, and delivery leadership.
Design and review scalable, secure, and cost-effective data architectures, data models, lakehouse platforms, and cloud-based data solutions.
Support data cataloging, metadata management, data governance, data quality, and compliance practices across enterprise data platforms.
Evaluate and implement AI/ML-enabled features, automation opportunities, and intelligent data engineering practices to improve platform efficiency and business outcomes.
Drive best practices in Data Engineering, Cloud, Data Architecture, DevOps, CI/CD, performance optimization, and engineering excellence.
Participate in technical assessments, solution reviews, interviews, and capability-building initiatives for Data Engineering roles.
Requirements
12+ years of overall experience in Data Engineering, Data Platforms, Cloud Technologies, AI-enabled data solutions, and enterprise architecture.
4+ years of experience in a Technical Leadership role, including team management, mentoring, delivery ownership, and stakeholder collaboration.
Strong hands-on expertise in Databricks and Snowflake.
Strong experience with AWS, Azure, or GCP cloud platforms.
Strong knowledge of Spark, PySpark, Python, SQL, and large-scale data processing frameworks.
Experience in data modeling, data cataloging, metadata management, data governance, and enterprise data management practices.
Experience building Data Lakes, Data Warehouses, Lakehouse platforms, ETL/ELT pipelines, and orchestration workflows.
Understanding of AI/ML concepts, GenAI-enabled data solutions, automation use cases, and AI-driven engineering practices.
Strong team management, stakeholder management, client engagement, communication, and problem-solving skills.