Job Description GCP Data Architect

Location: Gurgaon (Hybrid)

About the Role:

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 organization'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.

Key Responsibilities:

1. 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 programs.

2. 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.

- Optimize 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.

3. Data Engineering & 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 modernization of legacy data platforms to cloud-native architectures.

4. Analytics & 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.

5. Data Governance & Security:

- Define enterprise data governance standards and best practices.

- Implement data quality frameworks, validation rules, and monitoring processes.

- Contribute to metadata management, data cataloging, and lineage initiatives.

- Establish data contracts, SLAs, and governance policies across business domains.

- Ensure compliance with organizational security and regulatory requirements.

6. 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.

Required Skills & Experience:

1. Essential:

- 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 modeling 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.

2. Preferred:

- Experience working in Agile product organizations.

- Exposure to Vertex AI or cloud-based machine learning services.

- Familiarity with modern data catalog and governance tools such as Collibra, DataHub, or 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.

What We're Looking For:

We are looking for a technology leader who is passionate about building modern, scalable data platforms on Google Cloud Platform. You should have the ability to balance strategic architecture with hands-on technical expertise, collaborate effectively with cross-functional teams, and drive best practices in cloud data engineering and governance.

The ideal candidate thrives in a fast-paced environment, enjoys solving complex data challenges, and has a strong foundation in designing enterprise data ecosystems that support business intelligence, analytics, and future innovation.