Lead Data Solution Architect

About the role

We are seeking a seasoned Lead Data Solutions Architect to own the end‑to‑end design and delivery of enterprise‑scale data, BI, big data and AI platforms. You will shape the data architecture roadmap, ensure solutions are secure, scalable and aligned with business goals, and guide cross‑functional teams through the full software development lifecycle.

Key responsibilities

• Define and drive the architecture strategy for data, BI, big data and AI solutions from source to consumption.
• Create high‑level and low‑level designs, architecture blueprints and technical specifications.
• Maintain a roadmap that transitions the organization from current‑state to target‑state data architecture.
• Design and implement enterprise data hubs, data warehouses, Hadoop/Big Data platforms, analytics and AI solutions.
• Assess technical feasibility, identify risks and recommend appropriate technologies.
• Collaborate with local and international teams to enforce architecture standards and governance.
• Provide technical leadership to engineering, infrastructure and operations teams.
• Lead architecture reviews, ensure compliance with policies and define data security, retention and archiving strategies.
• Mentor team members, assign responsibilities and support professional development.
• Manage multiple projects and stakeholder expectations to deliver high‑quality outcomes.

Required profile

• 15–18 years of overall IT experience, primarily in data architecture.
• Bachelor’s degree in Computer Science, IT, Engineering or related field; Master’s preferred.
• Proven expertise in enterprise data architecture, data hub design and big‑data platforms.
• Extensive experience designing data warehouses, BI reporting, analytics and AI solutions.
• Strong background in data modeling, database architecture and data administration.
• Deep understanding of ETL/ELT processes, data integration tools and both relational and NoSQL databases.
• Knowledge of data security, governance, access controls, retention and archival practices.

Required skills

• Hadoop and related big‑data ecosystem technologies
• Data warehouse design and implementation
• BI reporting and analytics platforms
• Artificial intelligence and machine‑learning integration
• Data modeling and database architecture
• ETL/ELT tools and processes
• Relational databases (e.g., Oracle, SQL Server, MySQL)
• NoSQL databases (e.g., MongoDB, Cassandra)
• Data integration and migration tools
• Data security, governance and access‑control frameworks