Data Architect

JOB SUMMARY

This a critical role responsible for leading and shaping the data architecture strategies within our organization. This position exists to guide the design and implementation of data architecture for the data engineering teams, collaborate with external vendors, and support the enhancement and maintenance of legacy data applications and integrations. In addition to supporting traditional enterprise data management functions, the Data Architect designs solutions that also enable advanced analytics initiatives—including AI, ML, and Data Science—by developing comprehensive data models and integrating modern, legacy, and big data technologies.

The end goal is to ensure the data architecture aligns with business needs, supports data-driven decision-making, and facilitates the smooth operation and scalability of our data systems.

• Develop, document, and maintain data architecture standards, models, and guidelines that ensure scalable, secure, and efficient data storage, integration, and retrieval across the enterprise.
• Leverage industry‑standard architectural frameworks (e.g., TOGAF) to guide design decisions and ensure best practices.
• Design architectures that enable high‑performance analytics, including real‑time data feeds and predictive modeling.
• Partner with external vendors to enhance data warehouse capabilities and implement new features.
• Select appropriate technologies and tools in collaboration with vendors.
• Guide the enhancement and maintenance of legacy data applications and integrations.
• Strategize the modernization of legacy systems where necessary.
• Work with various departments to understand and fulfill their data requirements.
• Promote data literacy and empowerment throughout the organization.
• Contribute to long‑term strategic planning for the department’s data architecture initiatives.
• Develop comprehensive documentation, including data models, architecture diagrams, integration flows, and technical specifications to support effective communication and execution of the data strategy.
• Continuously evaluate emerging trends and technologies, integrating innovative solutions that enhance the organization’s data ecosystem.
• Design data architecture solutions that support advanced analytics initiatives, ensuring that infrastructures can effectively serve AI, ML, and Data Science workloads.
• Optimize data pipelines for performance and scalability, including the design of architectures that support real‑time data processing and model deployment.
• Ensure that architecture designs facilitate robust data quality processes through automated testing, monitoring, and error handling.
• Integrate comprehensive metadata capture and cataloging solutions to provide transparency and traceability across data movements and transformations.
• Adhere to and enforce data governance, security, and compliance standards, ensuring all architectures meet regulatory requirements.
• Perform other job‑related functions as assigned.

DEGREE TYPE:
Bachelor’s Degree

FIELD(S) OF STUDY:
Business Management, Computer Science, Industrial Engineering, or a related field; or an equivalent combination of relevant work experience and education.

EXPERIENCE:

Minimum of 10+ years of progressive experience architecting and delivering enterprise data platforms in cloud‑first environments. Deep expertise designing scalable, cloud‑native data architectures (e.g., Snowflake, dbt, modern ELT), including CI/CD, observability, security, and cost optimization. Strong command of advanced data modeling techniques (dimensional, data vault, domain‑driven design) and building governed, reusable data products. Proficiency in SQL and modern programming/scripting languages (e.g., Python) to enable automation, orchestration, and platform extensibility.

Experience enabling AI/ML and advanced analytics use cases, including preparation of structured and unstructured data for LLMs, semantic layers, and real‑time or event‑driven architectures. Proven ability to lead architecture in Agile product delivery environments and collaborate cross‑functionally with engineering, analytics, Dev Ops, and business stakeholders.

COMPETENCIES &

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