Data Architect/Principal Data Engineer

Santa Clara, CA

Requirements

The Data Architect will play a pivotal role in the design, development, and management of data architectures that support the organization's data engineering and data science projects. The ideal candidate will have extensive experience in data modeling, data warehousing, and cloud platforms, particularly Snowflake and AWS. This role requires a deep understanding of data security, governance, and best practices in data management, as well as proficiency in data engineering tools such as Python, Airflow, and PL/SQL. The Data Architect will also collaborate closely with data engineers, data scientists, and business intelligence teams to ensure that data solutions meet the organization's analytical and operational needs.

Data Architect should have degree in Information systems ,computer science or equivalent education with 8+ years of experience architecting and developing end to end data and analytic solutions.

Job Responsibilities

Data Architecture & Modeling:

• • Design and implement scalable, efficient, and secure data architectures that support data engineering and data science projects.
  • Develop and maintain conceptual, logical, and physical data models for structured and unstructured data.
  • Ensure that data models and architecture align with business requirements and industry best practices.

Cloud Platforms & Data Engineering:

• • Design and manage data solutions on AWS, utilizing services such as S3, EMR, and other relevant AWS data services.
  • Develop and maintain data pipelines using Python and Apache Airflow for ETL/ELT processes.
  • Ensure data workflows are optimized for performance and scalability.
  • Optimize data storage and retrieval processes to support analytics and reporting needs.

Data Security & Governance:

• • Implement and enforce data governance policies and procedures to ensure data accuracy, integrity, and security.

Collaboration & Stakeholder Management:

• • Collaborate with data engineers, data scientists, and business intelligence teams to design and implement data solutions that meet business needs.
  • Work with business stakeholders to understand data requirements and translate them into technical specifications.
  • Provide guidance and mentorship to junior team members in data architecture and engineering best practices.

Business Intelligence & Reporting:

• • Collaborate with the BI team to ensure that data models support reporting and analytics needs, particularly in tools like Power BI.
  • Ensure that data is available, accurate, and accessible to end-users for reporting and decision-making.

AI and Gen AI Expertise:

• • Collaborate with data science and AI teams to design data architectures that support AI and Gen AI initiatives.
  • Stay informed about the latest advancements in AI and Gen AI technologies and explore opportunities to integrate these into the organization's data strategy.
  • Contribute to the continuous improvement of AI-related data processes and infrastructure, ensuring scalability and efficiency.

Technical Expertise & Continuous Improvement:

• • Stay up-to-date with emerging technologies and trends in data architecture, engineering, analytics, AI, and Gen AI.
  • Continuously evaluate and improve existing data architectures to enhance performance, scalability, and security.
  • Drive innovation by exploring and recommending new tools and technologies to improve the organization's data capabilities.