Full-Time GCP Data Architect

As a GCP Data Architect, you will be a senior technical leader responsible for designing, implementing, and overseeing robust data architectures on Google Cloud Platform (GCP). You’ll guide customers through complex data and AI transformations, shape data strategy, and lead technical delivery for critical projects. Your work will span advisory, architecture, governance, and implementation, with a strong focus on data platform modernization, AI enablement, and operational excellence.

This role also includes mentoring engineers, working closely with Google teams, contributing to strategic pre-sales efforts, and helping clients realize business value through cloud-native data and AI solutions.

Key Responsibilities:

1. Lead the design and implementation of secure, scalable, and cost-effective data architectures on GCP.
2. Collaborate with client stakeholders to define enterprise data strategies, roadmaps, and reference architectures.
3. Architect and implement advanced data pipelines (ETL/ELT) and AI-ready data platforms (e.g., Data Lakes, Data Mesh).
4. Guide modernization of legacy systems to cloud-native solutions using tools like BigQuery, Cloud SQL, and Dataproc.
5. Enable AI/ML capabilities through robust MLOps platforms, model management pipelines, and data governance strategies.
6. Drive adoption of best practices around CI/CD, DataOps, and MLOps on GCP.
7. Participate in delivery oversight, ensuring technical excellence across engagements.
8. Mentor junior engineers and lead knowledge-sharing initiatives across teams.
9. Support proposal writing, solution demos, and technical workshops during pre-sales cycles.

Technical Expertise:

• 7+ years of experience in data engineering, architecture, or analytics roles.
• Strong expertise with Google Cloud services: BigQuery, Dataflow, Pub/Sub, Cloud Storage, Vertex AI, Cloud Composer, etc.
• Deep knowledge of data modeling, data warehousing, distributed systems, and real-time/stream processing.
• Proficiency in SQL, Python, and data transformation tools.
• Experience implementing Data Lakes, Lakehouses, or Data Mesh architectures.
• Hands-on experience with orchestration frameworks (e.g., Airflow, Composer).
• Good understanding of ML lifecycle management and MLOps tools.
• Experience with Infrastructure as Code (Terraform, Deployment Manager) and CI/CD for data systems.

Strategic and Leadership Skills:

• Proven ability to shape data strategy and architectural direction at enterprise level.
• Strong stakeholder engagement and consulting experience.
• Ability to present technical concepts to both engineering teams and C-level stakeholders.
• Experience leading cross-functional teams in hybrid (onshore/offshore) environments.

Certifications (preferred):

• Professional Data Engineer or Professional Cloud Architect (Google Cloud).
• Relevant certifications in AI/ML or Kubernetes (e.g., TensorFlow Developer, CKAD).

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