Associate Principal Engineer (Data Architecture)

👋🏼We're Nagarro.

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at a scale — across all devices and digital mediums, and our people exist everywhere in the world (18500+ experts across 39 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!

Requirements

• Experience : 9+ years
• Strong experience in Data Architecture, Enterprise Data Architecture or related data engineering roles.
• Relevant hands-on experience in Google Cloud Platform (GCP) and designing large-scale enterprise data platforms.
• Strong expertise in GCP services, including BigQuery, Google Cloud Storage, Dataflow, Pub/Sub, Eventarc, Cloud Run, Cloud Functions, Cloud Workflows and IAM.
• Strong knowledge of enterprise data modelling, including conceptual, logical and physical data models.
• Expertise in dimensional modelling, relational modelling, Data Vault and domain-oriented data modelling approaches.
• Strong experience in designing and implementing scalable Data Lake, Lakehouse and Data Warehouse architectures.
• Hands-on experience with data ingestion, ETL/ELT, data integration and processing frameworks.
• Strong understanding of batch, streaming and real-time/near-real-time data processing architectures.
• Experience with Pub/Sub, Eventarc, Kafka or other event-driven and event-streaming technologies.
• Strong SQL skills and expertise in BigQuery data modelling, partitioning, clustering and query performance optimization.
• Experience in designing enterprise data architectures, data products, semantic layers and reusable data models.
• Knowledge of data governance, metadata management, data lineage, data quality and data cataloguing.
• Strong understanding of cloud security, IAM, encryption, data privacy, access controls and least-privilege principles.
• Experience in designing API-based, event-driven and file-based integration architectures.
• Ability to translate complex business requirements into scalable conceptual, logical and physical data architectures.
• Experience collaborating with global stakeholders across Enterprise Architecture, Data Engineering, AI/ML, Cloud Engineering and Cybersecurity teams.
• Strong communication, stakeholder management, architecture governance and technical leadership skills.
• Good to have experience with Dataplex, Data Catalog, Looker/LookML, Vertex AI and Google Cloud AI services.
• Good to have exposure to Generative AI, RAG, vector search, embeddings, Machine Learning and AI-ready data architectures.
• Exposure to Terraform, Infrastructure as Code, Kubernetes, GKE, CI/CD and GitOps practices is desirable.
• Experience integrating enterprise applications such as SAP, Salesforce, CRM, e-commerce, marketing and supply-chain platforms is preferred.
• Exposure to KVYOS and enterprise knowledge architecture is an added advantage.
• Experience in Retail, CPG, FMCG, Beauty or Consumer Products domains and global multi-country data platforms is desirable.

Responsibilities

• Define, drive and evolve enterprise data architecture, target-state architecture and technology roadmaps across global business and technology domains.
• Design scalable, secure, governed and AI-ready enterprise data platforms using Google Cloud Platform.
• Establish enterprise architecture standards, principles, reference architectures, reusable design patterns and technology guidelines.
• Architect modern GCP data platforms leveraging BigQuery, Google Cloud Storage, Dataflow, Pub/Sub, Eventarc, Cloud Run, Cloud Functions and Cloud Workflows.
• Design cloud-native, serverless, event-driven, multi-cloud and hybrid data architectures.
• Define scalable data ingestion, integration, processing, storage, analytics and consumption architectures.
• Architect batch, streaming and near-real-time data pipelines for enterprise data sources.
• Design and implement event-driven integration patterns using Pub/Sub, Eventarc and Kafka.
• Define integration strategies for SAP, Salesforce, e-commerce, CRM, marketing, retail, supply-chain and external data platforms.
• Establish enterprise conceptual, logical and physical data models using dimensional, relational, Data Vault and domain-oriented modelling approaches.
• Define canonical data models, enterprise data standards, semantic layers and common business metrics and KPIs.
• Drive the creation, implementation and lifecycle management of reusable domain-oriented data products.
• Design and optimize BigQuery data architectures, including data models, partitioning, clustering, workload management and query performance.
• Leverage Dataplex and Data Catalog for enterprise data discovery, metadata management, governance and lineage.
• Define data quality, observability, monitoring and operational standards across the data lifecycle.
• Design secure GCP architectures using IAM, Workload Identity, encryption, VPC Service Controls and least-privilege access.
• Establish data classification, masking, privacy, access-control and row/column-level security mechanisms.
• Ensure compliance with global data privacy, security and regulatory requirements.
• Architect AI/ML-ready data platforms supporting Generative AI, Machine Learning and Agentic AI use cases.
• Design data pipelines for AI/ML training, inference, feature engineering, RAG, vector search and enterprise knowledge management.
• Integrate enterprise data platforms with Vertex AI and other Google Cloud AI services.
• Enable trusted and governed enterprise data for personalization, consumer analytics, marketing intelligence and business insights.
• Collaborate with Enterprise Architecture, Data Engineering, AI/ML, Cybersecurity, Cloud Engineering and business teams to deliver enterprise-wide data solutions.
• Lead architecture workshops, technical discussions and design reviews with global stakeholders.
• Evaluate emerging technologies, recommend suitable platforms and drive build-versus-buy decisions.
• Review solution architectures, provide technical governance and ensure alignment with enterprise architecture standards.
• Mentor data architects, data engineers and technical leads while promoting architecture best practices.
• Communicate complex architectural concepts, technical strategies and recommendations effectively to senior leadership.

## Qualifications

Bachelor’s or master’s degree in computer science, Information Technology, or a related field.