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.