Leadership and Collaboration:

• Lead and inspire cross-functional teams, including data engineers, data scientists, ML engineers, and BI analysts, fostering a culture of collaboration, innovation, and continuous improvement.

Enterprise Architecture:

• Develop and maintain enterprise-level architectural blueprints for cloud and data platforms, ensuring interoperability and scalability.
• Create a cohesive architecture that integrates data platforms, ML systems, and business intelligence tools.

Cloud and Data Platform Engineering:

• Design and implement cutting-edge cloud architectures using AWS, Azure, or GCP, focusing on scalability, security, and cost optimization.
• Build and optimize modern data platforms using Databricks or Snowflake for advanced analytics and real-time data processing.

Data and ML Integration:

• Collaborate with ML teams to deploy machine learning pipelines and operationalize AI models within enterprise systems.
• Provide architectural support for ML initiatives, including data preparation, feature engineering, and model lifecycle management.

Data Warehousing and Modeling:

• Architect enterprise data warehouses with robust data models, ensuring high performance and reliability for analytics workloads.

DevOps and DataOps Practices:

• Establish and enforce DevOps and DataOps pipelines to automate deployments, enhance agility, and ensure operational excellence.

Governance, Security, and Compliance:

• Implement robust security measures to protect sensitive data across platforms, adhering to privacy standards such as GDPR and CCPA.

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
• Experience: 
  • 11+ years in data architecture, cloud platforms, and advanced analytics.
  • Hands-on experience with at least two cloud platforms (AWS, Azure, or GCP) is mandatory.
  • Expertise in Databricks or Snowflake is required.
  • Comprehensive understanding of data warehousing, data modeling, and data integration.

Technical Skills:

• Proficient in programming languages such as Python, SQL, or Scala.
• Familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-learn is a plus.
• Experience with DevOps tools like Terraform, Kubernetes, or CI/CD platforms.

Preferred Qualifications:

• Certifications in AWS, Azure, GCP, Databricks, or Snowflake.