Data Architect

Philip Morris International•Tampa, FL

22h•Hybrid

About The Position

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. The ideal candidate is not just a builder but a decision-maker: someone who defines modeling standards, selects technologies, shapes governance frameworks, and ensures our data architecture is scalable, secure, and AI-ready. You will own the architecture blueprint that every downstream team depends on.

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.
Legally authorized to work in the U.S.

Nice To Haves

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.

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.
Architect AI-ready data frameworks that enable 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.