Principal Data Architect

Overview

• Define and maintain the enterprise data architecture roadmap aligned with the firm’s technology strategy and business objectives.
• Develop reference architectures, design patterns, and reusable components for data ingestion, transformation, modeling, and analytics.
• Partner with domain engineering and analytics teams to design fit‑for‑purpose, interoperable data solutions that align with enterprise standards.
• Lead architectural review sessions and ensure governance alignment across all domains.
• Serve as an advisor to leadership on data strategy, modernization, and investment prioritization.

• Architect and optimize data warehouse and data lakehouse solutions leveraging modern cloud data platforms (Snowflake, Databricks, Azure/AWS) integrated with on‑prem databases.
• Lead enterprise‑wide data modeling efforts (conceptual, logical, and physical) to ensure consistency, performance, and scalability across domains.
• Champion the use of canonical models and metadata standards to support semantic alignment and data product reuse.
• Design robust data warehouse architectures that support analytical, regulatory, and operational workloads, with a strong foundation in dimensional modeling and data vault methodologies.
• Collaborate with BI and Analytics teams to define semantic and business layers that enable self‑service analytics.

• Define and implement the enterprise MDM strategy ensuring consistency and accuracy of critical master and reference data (Client, Product, Account, Instrument, Legal Entity).
• Integrate data quality, metadata, and lineage frameworks within all architectural designs.
• Partner with governance and stewardship teams to enforce data ownership, classification, and privacy controls.
• Promote the ’data as a product’ mindset across business domains.
• Lead cloud migration initiatives for legacy data platforms (SQL Server, Oracle, and other on‑prem systems) to modern cloud environments.
• Define migration patterns, cut‑over strategies, and hybrid data access architectures.
• Partner with infrastructure and Dev Ops teams to implement CI/CD pipelines, Infrastructure‑as‑Code, and automated provisioning for data platforms.
• Ensure designs address scalability, security, cost optimization, and resiliency.

• Maintain deep familiarity with legacy database technologies, particularly Microsoft SQL Server, and design hybrid patterns that enable interoperability with modern cloud solutions.
• Provide guidance on data extraction, replication, and real‑time synchronization between legacy and cloud systems.
• Serve as a subject‑matter expert in SQL Server architecture, performance tuning, and optimization as part of the broader modernization roadmap.

• Architect AI/ML‑ready data environments by ensuring pipelines and models support feature engineering, versioning, and reproducibility.
• Collaborate with data scientists and ML engineers to define data provisioning, model training, and inferencing pipelines integrated into enterprise data architecture.
• Define data lineage, observability, and quality frameworks that ensure trust in …
• Nice to have:
  Hands‑on exposure or background in machine learning, AI model lifecycle management, or MLOps frameworks (e.g., Sage Maker, Azure ML, MLflow).

• Partner with technology and analytics teams across Investments, GTM/Sales (Retail & Institutional), Marketing, Finance, HR, Risk & Performance, and Legal to deliver scalable data products.
• Translate business requirements into logical and physical data models, reusable domain data pipelines, and shared data assets.
• Drive architectural consistency and interoperability across verticals.

• Lead modernization initiatives to transition from legacy on‑prem systems to cloud and hybrid architectures.
• Introduce event‑driven and streaming patterns (Kafka, Event Hubs) where real‑time data is required.
• Support adoption of federated data architecture principles (Data Mesh) within defined enterprise guardrails.

• 10+ years of experience in data architecture, data engineering, or enterprise data solution design.
• 10+ years of experience in SQL and advanced concepts.
• 3+ years with DBT and familiarity with advanced concepts.
• Proven expertise in data modeling and data warehouse design (3NF, dimensional, Data Vault).
• Hands‑on experience with Master Data Management (MDM) strategy and implementation.
• Demonstrated success leading cloud migration projects and designing hybrid architectures.
• Strong proficiency in SQL Server and relational database optimization.
• Knowledge of metadata management, data governance, data quality, and lineage.
• Excellent communication and stakeholder management across technical and business domains.