Principal Data Architect

The Principal/ Lead Data Architect plays a critical role in defining and enabling the enterprise data foundation that supports advanced analytics and AI-driven solutions. This role operates at the intersection of data strategy, platform architecture, and delivery execution, ensuring that data platforms are scalable, secure, and aligned with business priorities.

As a hands-on technical leader, the Lead Data Architect partners closely with executive stakeholders, data platform teams, AI and delivery pods, and application teams to ensure data is trusted, accessible, and fit for both decision-making and AI consumption. This is not a purely advisory or governance-only role—the position requires active involvement in architectural design, platform enablement, and solution delivery.

Will define and implement enterprise data architecture and strategy aligned with business objectives and AI-driven use cases.

Design scalable, secure, and flexible data platforms that support analytics, machine learning, and AI workloads.

Establish and maintain reference architectures for data warehouses, data lakes, lake houses, and AI-ready data platforms.

Provide hands-on architectural leadership across platforms such as Snowflake and Databricks, defining clear usage patterns and best practices.

Architect and oversee end-to-end data solutions, including ingestion, transformation, storage, modeling, and consumption layers.

Design and optimize ETL/ELT and data integration pipelines supporting batch and near–real-time processing.

Enable consistent data modeling practices (conceptual, logical, and physical) to support analytics, reporting, and AI use cases.

Collaborate with data scientists, ML engineers, and delivery teams to ensure data platforms are optimized for AI model training, inference, and monitoring.

Ensure data quality, lineage, observability, and accessibility across enterprise data domains. ? Establish pragmatic data governance standards covering security, privacy, compliance, and data ownership while enabling rapid delivery.

Partner with data-owning teams to define stewardship, access patterns, and accountability. Promote Dev Ops and Data Ops best practices, including CI/CD for data pipelines, version control, automated testing, and validation.

Act as a technical leader and trusted advisor to stakeholders, translating complex data and AI concepts into clear, actionable guidance.

Mentor and guide data engineers and architects, fostering a culture of quality, innovation, and continuous improvement. Requirements

Proven experience designing and delivering enterprise-scale data platforms that support analytics and AI initiatives.

Strong hands-on experience with modern data architecture patterns, including:
Data warehouses, Data lakes, Lakehouse architectures, and Medallion architecture.

Expertise in data modeling and schema design, including conceptual, logical, and physical models. Experience designing high-performance analytical schemas in Snowflake (e.g., star and snowflake schemas).

Extensive hands-on experience with modern data platforms such as Snowflake and Databricks. Proficiency in SQL and Python for building, optimizing, and maintaining scalable data pipelines. Strong understanding of data integration patterns, including APIs, event-driven architectures, streaming, and ETL/ELT workflows.

Experience implementing enterprise data governance practices related to data quality, security, and compliance.

Excellent communication and collaboration skills, with the ability to influence cross-functional teams and align technical solutions to business goals.