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Russell Investments · 2 weeks ago

Principal Enterprise Data & Analytics Architect

Seattle, WA

Full-time

Hybrid

Lead/Staff

$160K/yr - $190K/yr

10+ years exp

Russell Investments is seeking a Principal Enterprise Data & Analytics Architect to join their Enterprise Data Office within the Technology organization. This role will lead the design, implementation, and modernization of enterprise data and analytics architecture, ensuring alignment with business objectives and driving excellence in data modeling and governance.

Financial Services

No H1B

U.S. Citizen Only

Responsibilities

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 DevOps 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 AI/ML outputs

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

Qualification

Data architectureCloud-based data platformsData modelingMaster Data ManagementSQL Server optimizationDBTData governanceData qualityAI/ML architectural patternsData observability toolsStakeholder managementCommunication skillsCollaborationMentoringStrategic thinking