Description

• Develop enterprise data and analytics architecture, including best practices, guiding principles, and

design patterns for new technology solutions aligned with the organization’s 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.

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• 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.

Qualifications

Education

• Bachelor’s degree in Statistics, Computer Science, Information Technology, or equivalent related

experience (required).

• Master’s degree in Computer Science, Data Science, Information Systems, or a related technical

discipline (preferred).

Experience

• 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.

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• 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

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non-technical audiences, and to mentor cross-functional team members on architectural best

practices.

Work Environment / Physical Requirements

• CBS - Pune office-based or Pune city-based for remote/hybrid work environment with access to

necessary compute resources.

• Standard working hours with flexibility to accommodate project deadlines and team members

across multiple time zones.

• Ability to sit for extended periods and use a computer for the majority of the workday.

Competencies

• Strategic Vision & Innovation: Actively explores emerging data, analytics, and generative AI

technologies to define forward-looking enterprise architecture strategies that reduce solution

complexity, accelerate time to market, and deliver measurable business impact.

• Technical Excellence: Maintains exceptionally high standards for architectural design quality,

pattern consistency, and engineering best practices across the five foundational pillars: data

platforms, analytics delivery, data security, artificial intelligence, and data management and

governance.

• Collaboration & Teamwork: Works effectively with cross-functional teams including business

stakeholders, solution architecture teams, data engineers, and application development teams to

ensure technology achieves desired business outcomes across initiatives of varying scope and

complexity.

• Adaptability: Thrives in a fast-paced environment where data technologies, cloud platform

capabilities, and business requirements evolve rapidly, adjusting architectural strategies to align

with shifting organizational priorities and emerging industry trends.

• Mentoring & Thought Leadership: Champions knowledge sharing by mentoring cross-functional

team members on architectural best practices, guiding proof-of-concept initiatives, and fostering a

culture of continuous learning and self-development across the data and analytics ecosystem.

• Results Orientation: Focuses on delivering production-ready, scalable architectural solutions and

roadmaps that generate tangible business value, advancing overall architectural maturity and

enabling reusable, enterprise-wide data and analytics capabilities

Key Skills

Education

Bachelor's or Master's degrees