Data Architect - GCP

We are looking for an experienced GCP Data Architect to lead the design and implementation of enterprise-scale data platforms on Google Cloud Platform (GCP)This is a strategic role responsible for defining the organisation's data architecture, enabling modern analytics, and building scalable, secure, and high-performance data solutions. The ideal candidate will have strong expertise in GCP data services, data architecture, data engineering, cloud-native technologies, and enterprise data governance. You will work closely with product, engineering, analytics, and business teams to build reliable data platforms that support reporting, analytics, and future AI/ML initiatives. The core responsibilities for the job include the following: Data Architecture: Design and maintain enterprise-wide data architecture, including conceptual, logical, and physical data models.Define scalable data lake, data warehouse, and lakehouse architectures on Google Cloud Platform.Establish enterprise data standards, naming conventions, and architecture best practices.Evaluate and recommend appropriate data storage and processing patterns for structured and semi-structured data.Drive architecture decisions for new business initiatives and digital transformation programmes. Google Cloud Platform (GCP): Design and implement cloud-native data solutions using GCP services such as BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, BigLake, Cloud Composer, Cloud Run, and GKE.Optimise data platforms for scalability, performance, reliability, and cost efficiency.Ensure secure and compliant cloud architectures by implementing IAM, encryption, and governance best practices.Collaborate with cloud engineering teams on Infrastructure as Code (Terraform preferred) and CI/CD automation. Data Engineering and Integration: Design and oversee enterprise ETL/ELT pipelines for batch and real-time data processing.Build scalable integration frameworks across multiple enterprise applications and data sources.Architect streaming data solutions using technologies such as Pub/Sub, Kafka, Spark, or Dataflow.Ensure end-to-end data lineage, monitoring, observability, and operational excellence.Drive modernisation of legacy data platforms to cloud-native architectures. Analytics and Data Enablement: Design semantic and analytical data models for enterprise reporting and business intelligence.Partner with analytics teams to develop trusted, reusable, and governed data products.Enable self-service analytics by delivering well-documented, high-quality datasets.Support data platforms that can serve advanced analytics and future machine learning initiatives. Data Governance and Security: Define enterprise data governance standards and best practices.Implement data quality frameworks, validation rules, and monitoring processes.Contribute to metadata management, data cataloguing, and lineage initiatives.Establish data contracts, SLAs, and governance policies across business domains.Ensure compliance with organisational security and regulatory requirements. Stakeholder Collaboration: Partner with product, engineering, business, and analytics stakeholders to understand data requirements.Lead architecture reviews, technical discussions, and solution design workshops.Translate complex business requirements into scalable technical solutions.Mentor data engineers and provide technical guidance across teams. Requirements: 10+ years of experience in data architecture, data engineering, or cloud data platform development.Strong hands-on experience with Google Cloud Platform (GCP).Expertise in BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, BigLake, and Cloud Composer.Strong understanding of data lake, data warehouse, and lakehouse architectures.Experience with data modelling methodologies, including relational, dimensional (Kimball), and data vault.Hands-on experience building large-scale ETL/ELT pipelines.Strong SQL expertise and programming experience in Python.Experience with distributed data processing frameworks such as Apache Spark/PySpark.Good understanding of streaming architectures and event-driven data processing.Experience with Infrastructure as Code (Terraform preferred).Strong understanding of data governance, security, and metadata management.Excellent communication and stakeholder management skills. Preferred: Experience working in agile product organisations.Exposure to Vertex AI or cloud-based machine learning services.Familiarity with modern data catalogue and governance tools such as Collibra, DataHub, and Alation.Experience with BI tools such as Looker, Tableau, or Power BI.Knowledge of Data Mesh principles and domain-driven data architecture.Exposure to modern AI/ML-enabled data platforms is an added advantage. .