Principal Data Architect Data Modeling

Principal Data Architect Data Modeling
Location: Remote
Experience: 15+ Years

Job Overview

We are seeking an experienced Principal Data Architect with deep expertise in enterprise data modeling, modern data architecture, and large-scale cloud data platforms. The ideal candidate will have a proven track record of designing and owning enterprise data solutions, leading cloud modernization initiatives, and establishing data governance and quality standards across complex environments.

This role requires a hands-on technical leader who can make architectural decisions, guide engineering teams, and build scalable, secure, and cost-efficient data platforms that support analytics, reporting, and AI/ML workloads.

Key Responsibilities

• Design and lead enterprise-scale data architecture across cloud-native lakehouse and data platform environments.
• Develop and govern dimensional, normalized, and Data Vault data models, making appropriate decisions around data grain, conformed dimensions, and modeling patterns.
• Architect data platforms using Databricks, Delta Lake, Snowflake, Apache Spark, PySpark, Kafka, and cloud services across AWS and Azure.
• Optimize complex SQL workloads, data pipelines, partitioning, clustering, and platform costs.
• Establish enterprise data governance, data quality, metadata management, master data management (MDM), and data lineage practices.
• Lead modernization and migration of legacy on-premises data warehouses and ETL systems to cloud-native platforms.
• Ensure data security, regulatory compliance, access controls, and governance across sensitive enterprise data environments.
• Design reliable batch and streaming data pipelines using technologies such as Spark Structured Streaming, Kafka, and Airflow.
• Collaborate with AI/ML teams on feature pipelines, training data preparation, and retrieval or vector-based data architectures.
• Define architecture standards, technical roadmaps, reusable frameworks, and engineering best practices.
• Partner with business stakeholders, engineering teams, and executive leadership to translate business requirements into scalable technical solutions.
• Support presales activities, including RFP/RFI responses, solution design, effort estimation, and Statements of Work (SOW).

Required Qualifications

• 15+ years of hands-on experience in data engineering, data architecture, and enterprise data platform development.
• Strong expertise in dimensional modeling, normalized modeling, and Data Vault methodologies.
• Extensive experience designing and implementing lakehouse or data mesh architectures.
• Advanced proficiency in SQL, Python, PySpark, and large-scale data processing.
• Hands-on experience with Databricks, Delta Lake, Snowflake, Apache Spark, Kafka, and Spark Structured Streaming.
• Strong knowledge of AWS data services, including S3, Glue, Redshift, Kinesis, Lambda, and related cloud-native technologies.
• Experience with orchestration, infrastructure automation, and CI/CD using tools such as Airflow, Terraform, and Git.
• Demonstrated ownership of enterprise data governance, data quality, metadata, lineage, and master data management.
• Experience with cloud migration, legacy warehouse modernization, and ETL transformation.
• Strong understanding of data security, privacy, and compliance in regulated or large-enterprise environments.
• Ability to lead architecture discussions, influence technical decisions, and communicate with senior stakeholders.

Preferred Qualifications

• Experience in financial services, payments, AML, risk, compliance, healthcare, cybersecurity, or large enterprise SaaS environments.
• Experience with data governance platforms such as Unity Catalog, Collibra, Alation, or Microsoft Purview.
• Familiarity with dbt, Great Expectations, BI semantic layers, Docker, Kubernetes, and observability platforms.
• Working knowledge of MLflow, feature stores, vector databases, knowledge graphs, LLM APIs, and RAG frameworks.
• Familiarity with Model Context Protocol (MCP) and agent data access or entitlement models.
• Experience in presales solutioning, RFP/RFI responses, SOW development, effort estimation, and CXO-level presentations.
• Relevant certifications in Databricks, AWS, or Snowflake.

Education

Bachelor s or Master s degree in Computer Science, Information Systems, Data Engineering, or a related quantitative discipline.