Associate Principal Engineer (Data Architecture)

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.

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

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