New opportunity

Solution Architect - ERP D2P Data & Analytics

Roche · Pune, India

About this role

The Solution Architect for ERP Demand-to-Pay (D2P) Data & Analytics will design scalable, business-aligned data architectures to support procurement, accounts payable, and travel & expense management. This role involves defining end-to-end data solution architecture, managing data platforms, and driving the strategy for data integration, sharing, and governance. You will lead data modeling efforts, including conceptual, logical, and physical models, and enable advanced analytics and AI solutions. Responsibilities include designing scalable AI pipelines for structured and unstructured data, incorporating NLP, embeddings, semantic search, RAG, Generative AI, and Agentic AI systems. You will also provide technical oversight to product teams, maintain technology roadmaps, and evaluate new technologies to manage technical debt. Required Qualifications: Bachelor's degree in Computer Science Engineering or related discipline. 5+ years of experience in Data Architecture with a track record of designing enterprise-grade analytical and AI products. 2+ years of experience in the pharmaceutical, biotechnology, or regulated healthcare industry. 1+ years of experience in AI Architecture and emerging technology. Mandatory technical skills include in-depth knowledge of Snowflake, DBT, and Talend. Proficiency in defining reference architectures, patterns, and non-functional requirements is essential. Preferred Qualifications: Professional certifications such as TOGAF Practitioner, CDMP (Associate), or Zachman Associate are considered a plus. Strong communication and stakeholder management skills are required to build consensus and drive technology trends.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

SnowflakeDBTTalendCortex AINLP pipelinesEmbeddingsSemantic searchRAGGenerative AIAgentic AIData ArchitectureData ModelingCloud ArchitectureData GovernanceData Mesh

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Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Designing Scalable ERP D2P Data Architecture
The core mandate is to architect end-to-end data solutions for procurement, accounts payable, and travel & expense management. You must ensure the architecture is business-aligned, scalable, and capable of integrating structured and unstructured data while adhering to enterprise standards and non-functional requirements.
Integrating Advanced AI and Analytics
You are responsible for enabling advanced AI capabilities within the D2P domain. This requires designing pipelines that incorporate NLP, embeddings, semantic search, RAG, and Agentic AI systems, ensuring these technologies integrate seamlessly with existing enterprise APIs and cloud-native platforms.
Data Governance and Domain Connectivity
You must support the Data Mesh framework by defining ownership, lifecycle management, and governance policies. This includes managing data privacy, risk, and anonymization obligations while ensuring D2P data is effectively shared and reused across other organizational data domains.
Technical Roadmap and Debt Management
You will provide technical oversight to product teams and maintain a technology roadmap. A key priority is evaluating new technologies to drive innovation while proactively identifying and managing technical debt to ensure long-term solution sustainability.

Likely challenges to think through

Balancing Innovation with Regulatory Compliance
Operating in a regulated healthcare environment requires strict adherence to data privacy and retention policies. Prepare to discuss how you maintain agility in AI development while ensuring that RAG and Generative AI implementations meet rigorous compliance and data security standards.
Managing Complexity in Hybrid Data Ecosystems
The role requires managing data across cloud, on-prem, or hybrid platforms using Snowflake, DBT, and Talend. You may face challenges in maintaining consistency and performance across these environments; prepare to explain your approach to defining reference architectures that mitigate this complexity.
Driving Consensus in a Decentralized Model
Implementing a Data Mesh model requires balancing domain-specific autonomy with enterprise-wide consistency. You will need to demonstrate strong stakeholder management skills to build consensus among diverse teams when trade-offs between local domain needs and global architectural standards arise.

Practice your answers

Technical practice

Designing Data Architectures for D2P Processes

What to coverExplain how you would design a scalable data architecture for Demand-to-Pay (D2P) processes, specifically addressing the integration of procurement, accounts payable, and travel data. Focus on how you ensure the architecture remains business-aligned while supporting both structured and unstructured data requirements.

Implementing RAG and Agentic AI in ERP Systems

What to coverDiscuss the architectural considerations for integrating Retrieval-Augmented Generation (RAG) and Agentic AI systems into an existing ERP data landscape. Address how you manage data quality, semantic search accuracy, and the orchestration of AI agents within a regulated environment.

Data Modeling and Semantic Layers

What to coverDescribe your approach to defining conceptual, logical, and physical data models for complex ERP domains. Explain how you design a semantic layer that effectively supports advanced analytics and AI applications while maintaining consistency across different business units.

Snowflake, DBT, and Talend Integration

What to coverHow do you leverage Snowflake, DBT, and Talend together to build robust, enterprise-grade data pipelines? Suggestion: Focus on how you divide responsibilities between these tools for ingestion, transformation, and storage to optimize performance and maintainability.

Data Mesh and Governance in Regulated Industries

What to coverExplain how you would implement a Data Mesh framework within a highly regulated pharmaceutical or healthcare context. Address how you balance decentralized domain ownership with the need for centralized data governance, privacy, and compliance.

Managing Technical Debt and Technology Roadmaps

What to coverHow do you evaluate new technologies for inclusion in a product roadmap while simultaneously managing existing technical debt? Suggestion: Discuss your criteria for assessing feasibility and the impact of new initiatives on the current application landscape.

Defining Non-Functional Requirements (NFRs)

What to coverWhat is your process for defining and enforcing NFRs and fitness functions in a large-scale data architecture project? Explain how you ensure these requirements are met throughout the lifecycle of a data product.

NLP Pipelines and Unstructured Data

What to coverDescribe the architecture of an NLP pipeline designed to process unstructured procurement or expense data. Focus on how you handle embeddings and ensure the data is prepared for downstream AI consumption.

Behavioural practice

Building Consensus Among Stakeholders

What to coverDescribe a situation where you had to explain a complex technical architecture to non-technical stakeholders to gain their buy-in. How did you tailor your communication to address their concerns and reach a consensus?

Driving Technology Adoption

What to coverProvide an example of a time you identified a new technology trend and successfully advocated for its adoption within your organization. How did you assess the feasibility and manage the transition?

Handling Conflicting Priorities

What to coverTell me about a time you had to balance the immediate needs of a product team with long-term architectural goals. How did you prioritize your efforts to ensure both business outcomes and technical integrity were maintained?

Working in a Regulated Environment

What to coverDescribe a challenge you faced while working in a regulated industry (e.g., pharma, healthcare) regarding data privacy or compliance. What actions did you take to ensure the solution met both business needs and strict regulatory standards?

Sharpen your knowledge

Topics to prepare

Data Mesh and Decentralized Architecture
The role requires supporting a Data Mesh-based management framework. Review the core principles of Data Mesh, specifically domain-oriented ownership and data as a product, to prepare for discussions on how to balance decentralized domain autonomy with enterprise-wide consistency.
AI Architecture for Enterprise ERP
Focus on integrating Generative AI, RAG, and Agentic AI into ERP workflows. Practice mapping business processes like Demand-to-Pay to AI-enabled data pipelines, ensuring you can explain how to handle structured and unstructured data within a regulated healthcare environment.
Non-Functional Requirements (NFRs) and Fitness Functions
Prepare to define and measure NFRs for data platforms. Review how to establish fitness functions to monitor technical debt and ensure scalability, reliability, and security in a cloud-native architecture.

Skills to revise

Snowflake and DBT Integration
As these are mandatory technical skills, practice designing data transformation workflows using DBT on top of Snowflake. Focus on modularizing data models and managing lineage in a complex ERP data environment.
Data Modeling (Conceptual, Logical, Physical)
Exercise creating data models that align with procurement and accounts payable business processes. Practice defining taxonomies and ontologies that support both traditional analytics and modern semantic layers for AI applications.
Talend for Data Integration
Review Talend's capabilities for enterprise-grade data integration. Prepare to discuss how you would use it to orchestrate data movement between on-premise ERP systems and cloud-based analytical platforms.

Your preparation checklist

Map D2P Data Architecture to Data Mesh Principles
Prepare a conceptual architecture diagram illustrating how you would manage Demand-to-Pay data using a Data Mesh approach. Focus on how you ensure domain ownership while maintaining global consistency, integration, and governance across procurement and accounts payable systems.
Design an AI-Driven Procurement Pipeline
Draft a technical proposal for an AI pipeline that processes unstructured procurement documents. Include how you would leverage Snowflake, DBT, and RAG architectures to enable semantic search and agentic AI systems, ensuring compliance with healthcare data privacy standards.
Prepare a Technical Debt and Roadmap Strategy
Create a framework for evaluating and prioritizing technical debt in legacy ERP data environments. Be ready to explain how you balance immediate business needs with long-term architectural scalability and the adoption of emerging technologies like Generative AI.
Formulate Stakeholder Management Scenarios
Develop examples of how you have previously built consensus among diverse stakeholders when implementing complex data governance or integration patterns. Focus on your ability to explain technical trade-offs and non-functional requirements to non-technical business partners.
Prepare Targeted Questions for the Interviewers
Draft questions regarding the current maturity of their Data Mesh implementation, the specific regulatory challenges faced by their D2P data domain, and how the team currently balances the integration of Cortex AI with existing Talend and Snowflake workflows.

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