Data Architect

Role & responsibilities

Data Architect

• Minimum professional experience as per the Level in enterprise data and analytics architecture, with a focus on designing scalable, reusable solutions aligned with strategic business objectives.
• Minimum of hands-on experience as per the Level defining architectural principles, patterns, and high-level designs for data platforms, analytics delivery, and AI/ML solutions in enterprise environments.
• Demonstrated experience integrating advanced analytics and artificial intelligence/machine learning models into existing data workflows using frameworks such as TensorFlow, PyTorch, or Scikit-learn across cloud-based environments.
• Experience leading proof-of-concept initiatives, capability maturity assessments, and the development of future-state architecture roadmaps for data and analytics ecosystems.
• Demonstrated experience advising on complex projects and initiatives involving architectural artifacts, deployment support, and generative AI solutions within an enterprise data and analytics landscape.
• Experience in a regulated industry (pharmaceutical, healthcare, or life sciences) is a plus.

Certifications (Required / Preferred)

• Relevant Cloud Provider Architect Certification (e.g., Microsoft Certified: Azure Solutions Architect Expert or AWS Certified Solutions Architect Professional) Required.
• The Open Group Architecture Framework (TOGAF) Certification Preferred.
• Certified Data Management Professional (CDMP) Preferred.
• Google Professional Cloud Architect Certification Preferred.

Knowledge, Skills & Abilities

• Expert-level proficiency in data and analytics computing methodologies, including the design and implementation of enterprise data architectures, data fabric frameworks, and common integration patterns such as batch, micro-batch, near real-time, and real-time processing.
• Strong experience with cloud platforms including Microsoft Azure, Amazon Web Services, and Google Cloud Platform, with the ability to architect scalable, secure, and cost-effective data and analytics solutions across multi-cloud environments.
• Proficiency in data pipeline and integration tools such as Azure Data Factory and Informatica Intelligent Cloud Services for designing end-to-end data ingestion, transformation, and orchestration workflows across on-premises and cloud environments.
• Hands-on experience with modern data platforms including Snowflake, Databricks, and Google BigQuery for architecting high-performance data storage, processing, and analytics solutions at enterprise scale.
• Solid understanding of artificial intelligence and machine learning frameworks and tools including TensorFlow, PyTorch, Scikit-learn, MLflow, Mosaic AI, and Amazon SageMaker with the ability to integrate advanced analytics and AI/ML models into existing data workflows.
• Proficiency in business intelligence tools such as Microsoft Power BI, Tableau, and Looker for designing analytics delivery architectures that enable self-service reporting and data-driven decision-making across the enterprise.
• Experience with streaming and event processing platforms including Apache Kafka and Azure Event Hubs for architecting real-time data pipelines and event-driven analytics solutions.
• Programming proficiency in Python and Structured Query Language (SQL) for prototyping architectural solutions, automating data workflows, and validating data platform configurations.
• Strong knowledge of data management, data security, and data governance principles, with the ability to define and maintain standard architectural principles, patterns, and processes that ensure enterprise-wide consistency and regulatory compliance.
• Excellent communication and stakeholder management skills with the ability to convey complex architectural concepts, design rationale, and technical recommendations to diverse technical and non-technical audiences, and to mentor cross-functional team members on architectural best practices.

• Develop enterprise data and analytics architecture, including best practices, guiding principles, and design patterns for new technology solutions aligned with the organizations strategic business objectives.
• Partner with business and solution architecture teams through implementation and operational support to ensure technology achieves desired business outcomes across initiatives of varying scope and complexity.
• Evaluate current architectures to identify weaknesses and formulate opportunities for improvement, defining interim and target-state strategies that advance overall architectural maturity.
• Integrate advanced analytics and artificial intelligence/machine learning (AI/ML) models into existing data workflows using frameworks such as TensorFlow, PyTorch, or Scikit-learn, ensuring seamless access and usability across the enterprise.
• Lead proof-of-concept initiatives to assess fit-for-purpose of potential solutions, leveraging data and analytics capability maturity assessments to build foundational future-state architecture and corresponding roadmaps.
• Contribute to the planning, design, and implementation of an enterprise data and analytics vision and strategy aligned with long-term strategic plans, enabling innovation while reducing solution complexity and time to market.
• Advise on complex projects and initiatives, taking full ownership of all necessary architectural artifacts, high-level designs, proof of concepts, and deployment support for data, analytics, and generative AI solutions.
• Guide the overall direction of architecture across the entire data and analytics ecosystem, focused on five foundational pillars: data platforms, analytics delivery, data security, artificial intelligence, and data management and governance.
• Define, document, and maintain standard architectural principles, patterns, and processes to ensure enterprise-wide consistency and enable reusable, scalable solutions.
• Validate architectural designs for accuracy, process review findings, propose solutions to remedy deficiencies, and confirm results in accordance with applicable data compliance requirements.
• Clarify technical inconsistencies and provide architectural expertise as necessary to cross-functional teams across multiple time zones.
• Communicate architectural requirements, design rationale, and technical recommendations to stakeholders and team members, adapting communication approaches for diverse audiences.

Document processes, standard operating procedures, and knowledge artifacts to ensure operational continuity during transitions, enabling seamless integration of evolving team structures .

Preferred candidate profile