GCP Data Architect

KANINI; United States; On-site; USD 120,000 - 160,000; Full time

Job summary

KANINI is seeking a strategic and technically proficient Data Architect to define and govern enterprise data architecture and integration frameworks. The role demands 8–12 years of experience in data engineering or architecture, focusing on scalable and secure data ecosystems.

The ideal candidate will command strong expertise in GCP cloud services, SQL, and data integration tools. Collaboration with engineering and business teams is essential for aligning data strategies with organizational goals.

Qualifications

• 8–12+ years of experience in data engineering or data architecture.
• Strong experience designing enterprise data architectures.
• Knowledge of APIs and microservices.

Responsibilities

• Ensure scalability and security across data platforms.
• Define standards for DevOps/DataOps practices.
• Architect robust data integration frameworks.

Skills

Data engineering

Data architecture

GCP cloud data platforms

SQL

Python

ETL/ELT tools

Tools

Google Cloud Dataflow

Informatica

Talend

Snowflake

BigQuery

Databricks

Job description

Work Authorization: Candidates must be legally authorized to work in the United States without the need for current or future employer sponsorship.

Scope

We are seeking a strategic and technically strong Data Architect to define, design, and govern enterprise data architecture and integration frameworks. This role is responsible for establishing scalable, secure, and high-performing data ecosystems that enable advanced analytics, business intelligence, and operational excellence.

The ideal candidate will bring deep expertise in data modeling, architecture patterns, integration strategies, and data governance, along with the ability to translate business needs into cohesive data solutions across complex, hybrid environments.

Key Responsibilities

• Ensure scalability, security, and performance across data platforms and pipelines.
• Define standards for DevOps/DataOps practices, including CI/CD, monitoring, and cost optimization.
• Drive adoption of modern data architecture patterns (Data Mesh, Lakehouse, etc.).
• Architect robust data integration frameworks across internal systems, APIs, SaaS platforms, and cloud services.
• Define standards for batch and real-time data processing, including event-driven architectures and streaming platforms (e.g., Kafka, Pub/Sub).
• Guide the design of ETL/ELT pipelines, ensuring scalability, reusability, and performance (execution may be handled by engineering teams).
• Define and enforce data governance frameworks, including data quality, stewardship, lineage, and metadata management.
• Establish enterprise standards for data validation, consistency, and observability.
• Partner with stakeholders to implement data catalogs, lineage tracking, and master data management (MDM) strategies.
• Ensure compliance with data privacy and regulatory requirements (e.g., HIPAA, GDPR).
• Collaborate with data engineers, data scientists, business leaders, and application teams to align architecture with business goals.
• Provide architectural guidance and oversight for data-related initiatives and projects.
• Mentor engineering teams on best practices in data architecture, modeling, and integration design.
• Act as a key advisor on data strategy and decision-making across the organization.

Qualifications

Required

• 8–12+ years of experience in data engineering, data architecture, or enterprise data platform roles.
• Strong experience designing enterprise data architectures, data models, and integration frameworks.
• Deep knowledge of GCP cloud data platforms ( GCP) and services such as Google Cloud Dataflow.
• Proficient in SQL, Python, and scripting for data manipulation and automation.
• Experience with ETL/ELT tools (Informatica, Talend, SSIS, etc.) from an architectural/design perspective.
• Strong understanding of APIs, microservices, and distributed systems.

Preferred

• Experience with streaming and event-driven architectures (Kafka, Pub/Sub, Kinesis).
• Hands‑on knowledge of modern data platforms (Snowflake, BigQuery, Redshift, Databricks).
• Experience implementing Data Lake, Lakehouse, or Data Mesh architectures.
• Familiarity with data governance tools, data catalogs, and metadata management solutions.
• Industry experience in healthcare, fintech, or e-commerce environments.