GCP Data Architect

This is a fully remote position, open to applicants in United States.

📋 Description

• Lead architectural decisions for GCP data platforms.

• Guarantee scalability, security, and performance across data platforms and their pipelines.

• Establish standards for DevOps/DataOps practices, which include CI/CD, monitoring, and cost optimization.

• Promote the adoption of contemporary data architecture patterns such as Data Mesh and Lakehouse.

• Design resilient data integration frameworks that span internal systems, APIs, SaaS platforms, and cloud services.

• Set standards for both batch and real-time data processing, incorporating event-driven architectures and streaming platforms (e.g., Kafka, Pub/Sub).

• Oversee the development of ETL/ELT pipelines, ensuring scalability, reusability, and performance (execution may be carried out by engineering teams).

• Define and uphold data governance frameworks, focusing on data quality, stewardship, lineage, and metadata management.

• Create enterprise standards for data validation, consistency, and observability.

• Collaborate with stakeholders to implement data catalogs, lineage tracking, and master data management (MDM) strategies.

• Ensure adherence to data privacy and regulatory requirements (e.g., HIPAA, GDPR).

• Work closely with data engineers, data scientists, business leaders, and application teams to align architecture with business objectives.

• Provide architectural guidance and oversight for data-related initiatives and projects.

• Mentor engineering teams on best practices in data architecture, modeling, and integration design.

• Serve as a primary advisor on data strategy and decision-making throughout the organization.

⛳️ Requirements

• 8–12+ years of experience in data engineering, data architecture, or roles focused on enterprise data platforms.

• Extensive experience in designing enterprise data architectures, data models, and integration frameworks.

• In-depth knowledge of GCP cloud data platforms and services, such as Google Cloud Dataflow.

• Proficient in SQL, Python, and scripting for data manipulation and automation.

• Familiarity with ETL/ELT tools (Informatica, Talend, SSIS, etc.) from an architectural/design standpoint.

• Strong understanding of APIs, microservices, and distributed systems.

• Experience with streaming and event-driven architectures (e.g., Kafka, Pub/Sub, Kinesis).

🏝️ Benefits

• Health insurance

• Retirement plans

• Paid time off

• Flexible work arrangements

• Professional development