Area(s) of responsibility

Senior/Lead Cloud and Data Architect

Position Overview

Birlasoft is seeking a visionary Senior/Lead Cloud and Data Architect with proven leadership capabilities to drive enterprise-level data solutions. The ideal candidate will have expertise in cloud platforms (AWS, Azure, or GCP), mandatory experience in Databricks or Snowflake, and a strong foundation in data warehousing, data modeling, DevOps, and DataOps. Additionally, the role demands a strategic leader with an understanding of machine learning (ML) concepts and the ability to function as an Enterprise Architect, providing guidance and alignment across diverse teams and initiatives.

Key Responsibilities

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.
• Act as the central technical point of contact for enterprise-wide data and analytics initiatives, ensuring alignment with business objectives.

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 while adhering to governance and compliance requirements.
• Provide strategic input on technology roadmaps, ensuring alignment with organizational vision and future-proofing investments.
• Evaluate and recommend emerging technologies and frameworks to enhance enterprise data capabilities.

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.
• Lead the development of end-to-end data pipelines, ensuring robust integration between data sources, platforms, and analytics tools.

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.
• Enable seamless integration of ML and analytics into business workflows, enhancing decision-making and operational efficiency.

Data Warehousing and Modeling:

• Architect enterprise data warehouses with robust data models, ensuring high performance and reliability for analytics workloads.
• Lead the development of advanced data models that cater to both analytical and operational requirements, emphasizing scalability and data quality.

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.