Data Architect

About the role

We are seeking a highly skilled and innovative Data Architect to join our Data Services team. This role blends hands-on technical depth with strategic architecture ownership — designing and governing the data blueprints that power our enterprise data platform, analytics, and AI/ML initiatives.

Responsibilities

• Design and maintain conceptual, logical, and physical data models (CDM/LDM/PDM) for enterprise data platform assets, ensuring alignment with business requirements and optimization for performance, scalability, and reuse.
• Define and enforce data modeling standards, naming conventions, and design patterns across the organization.
• Design database schemas, tables, views, indexes, and other database objects to support analytical and operational workloads.
• Collaborate with solution architects and data engineers to architect a robust, modern data platform supporting data integration, data quality, and data governance across cloud and hybrid environments.
• Define data flow diagrams, integration patterns, and orchestration strategies to ensure efficient and governed data movement between upstream and downstream systems.
• Work with data engineers to integrate data from multiple sources (structured and unstructured), ensuring consistency, quality, and lineage across the data ecosystem.
• Partner with data scientists and AI/ML engineers to design data architectures that support AI/ML model development, training, and deployment — ensuring seamless integration into data pipelines.
• Enable AI/ML & Advanced Analytics by enabling Business Intelligence (BI), machine learning, and generative AI use cases, including RAG patterns and LLM-powered applications.
• Implement explainable AI (XAI) principles to ensure transparency and trust in machine learning model outputs.
• Establish and enforce data governance standards, including data classification, PII handling, data lineage, access control, and metadata management.
• Create and maintain comprehensive documentation including data dictionaries, entity-relationship diagrams (ERDs), metadata catalogs, and architecture decision records.
• Ensure data models and architecture comply with data privacy regulations and organizational data governance policies.
• Monitor and optimize the performance of data models and database queries to ensure efficient data retrieval and processing at scale.
• Evaluate emerging technologies and architecture patterns (e.g., lakehouse, data mesh, data fabric) and make recommendations aligned with organizational strategy.
• Foster a culture of continuous learning and technical excellence within the team; mentor and guide data engineers and junior architects.

Requirements

• Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related field (Master's preferred).
• 7+ years of experience in data architecture and data modeling, including designing conceptual, logical, and physical data models across multiple functional domains (e.g., Sales, Finance, Supply Chain, Marketing).
• Strong proficiency in SQL and hands-on experience with modern cloud data platforms — specifically Snowflake and/or Databricks (Lakehouse architecture).
• Experience with ETL/ELT tools and frameworks (e.g., Matillion, dbt, Informatica, or similar) and data warehousing best practices.
• Proficiency in data modeling tools such as PowerDesigner, ER/Studio, ERwin, or similar.
• Working knowledge of Python for data processing, automation, or ML integration.
• Experience with AI/ML libraries and platforms (e.g., TensorFlow, PyTorch, Scikit-learn, Databricks ML, or AWS SageMaker) for supporting large-scale ML workloads.
• Solid understanding of data governance, data cataloging (e.g., Atlan, Alation, Unity Catalog), data quality frameworks, and compliance standards.

Qualifications

• Legally authorized to work in the U.S.

Skills

• Experience with cloud platforms (AWS, Azure, or GCP) and multi-cloud architectures.
• Familiarity with lakehouse architectures, Delta Lake, Iceberg, Snowpark, or similar modern data formats and frameworks.
• Knowledge of MLOps/LLMOps patterns, model observability, and GenAI architecture concepts (RAG, vector databases, agentic AI).
• Experience with data observability and pipeline orchestration tools (e.g., Monte Carlo, Great Expectations, Control-M, Apache Airflow).
• Relevant industry certifications (e.g., Snowflake SnowPro, Databricks Certified, AWS/Azure Data certifications, CDMP/TOGAF).
• Experience working in Agile/SAFe environments with cross-functional teams.
• Excellent communication skills with the ability to translate complex technical concepts for both technical and non-technical stakeholders.

Pay

Annual Base Salary Range: $104,000-$149,500

Schedule

Hybrid model of working that promotes flexibility in the workplace.