Principal Data Architect

Raymond James•Saint Petersburg, FL

8d•Hybrid

About The Position

The Principal Data Architect is a senior enterprise architecture and strategy leader within the Enterprise Data & Analytics Architecture team. This role defines and advances the enterprise data architecture vision, target-state patterns, data platform strategy, and adoption roadmap across on-premises and cloud environments. The Principal Data Architect partners with senior technology leaders, data engineering teams, enterprise data management, analytics consumers, security, risk, compliance, and business stakeholders to deliver scalable, secure, reusable, and business-aligned data capabilities.

Requirements

Deep, hands-on experience in wealth management, asset management, brokerage, private client services, or closely related financial services domains.
Proven ability to influence senior stakeholders, guide complex architectural decisions, mentor architects or senior engineers, and lead through ambiguity.
Expert level knowledge of Data Architecture, Data Modeling, Data Lake house and data warehouse design methodologies (star schema, snowflake schema, normalization, denormalization).
Proficient with database technologies: Oracle (including RAC, Exadata), SQL Server, AWS Redshift, and replication tools like Oracle Golden Gate and AWS DMS.
Advanced SQL, PL/SQL development, and database performance tuning skills.
Deep expertise in AWS Data Ecosystem—Athena, Iceberg, Lake Formation, Glue, EMR, Sagemaker, S3, Airflow, Aurora, Presto.
Skilled in scripting and automation (Shell, Python).
Data integration architecture: Ability to architect ETL/ELT, streaming, event-driven, API-based, file-based, and replication-based data flows, including data contracts, schema evolution, lineage, quality checks, and operational monitoring.
Data Lakehouse & Data Marketplace Architecture: Proven experience designing and operationalizing enterprise-scale data lake, Lakehouse, or data marketplace platforms, including governed data onboarding, metadata management, data product publishing.
AI Data Readiness & Semantic Data Enablement: Demonstrated ability to assess, structure, and curate enterprise data for AI, advanced analytics, and GenAI use cases, including defining semantic models, ontologies, knowledge graphs.
Bachelor's degree in Computer Science, MIS, or related field.
10+ years of progressive experience in data architecture, data engineering, database architecture, enterprise architecture, or large-scale data delivery.

Nice To Haves

AWS or relevant cloud certification highly preferred.

Responsibilities

Own reference architectures and design patterns for enterprise data Lakehouse and warehouse platforms, including AWS Redshift, Apache Iceberg, Oracle Exadata, S3, Glue, Lake Formation, Athena, EMR, Presto, Airflow, and related ecosystem capabilities.
Lead the design of logical, conceptual, and physical data models using ER Studio or similar tools. Establish modeling standards across normalized, dimensional, Data Vault, star, snowflake, and denormalized approaches. Resolve complex modeling issues that span multiple systems and business domains.
Create and maintain data architecture principles, design standards, reusable patterns, architecture decision records, reference implementations, and best-practice guidance that can be adopted across programs.
Define enterprise data access patterns, consumption models, and fit-for-purpose tool guidance for BI, advanced analytics, operational reporting, AI/ML, data products, APIs, and self-service use cases. Recommend appropriate access controls, semantic layers, and data sharing mechanisms.
Evaluate, rationalize, and guide selection of data tools, storage formats, integration technologies, metadata platforms, quality tooling, lineage capabilities, and cloud-native services. Balance innovation, cost, complexity, security, vendor risk, and operational maturity.
Define patterns for both batch and real-time data movement, including Kafka schemas, event-driven design, data contracts, schema governance, replication, CDC, ETL/ELT, medallion architecture layers, and data quality controls across pipelines.
Monitor emerging trends in cloud data platforms, lakehouse architectures, data mesh, data products, AI-ready data, metadata automation, data observability, and financial services data architecture. Recommend pragmatic adoption paths that strengthen enterprise capabilities.
Partner with Enterprise Data Management and governance teams to embed metadata, lineage, data quality, cataloging, ownership, privacy classification, retention, and stewardship expectations into platform and solution architecture.