Principal Enterprise Data & Analytics Architect

Russell Investments-posted 1 day ago
$160,000 - $190,000/Yr
Full-time • Senior
Hybrid • Seattle, WA
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Russell Investments is seeking a Principal Enterprise Data & Analytics Architect to join our Enterprise Data Office (EDO) within the Technology organization. The EDO functions as a horizontal capability, enabling enterprise data architecture, governance, engineering, and analytics enablement across all lines of business. This role will lead the design, implementation, and modernization of Russell’s enterprise data and analytics architecture in a hybrid environment (on-premises + cloud). The Architect will bridge enterprise technology teams (DevOps, Production Support, Cloud Platform) and domain-aligned teams across multiple business verticals — driving excellence in data modeling, master data management, data warehouse design, and AI/ML-ready architecture patterns.

  • 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.
  • 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
  • 5+ years designing cloud-based data platforms (Azure preferred).
  • 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.
  • Strategic thinker with ability to translate architecture into business outcomes.
  • Strong influencer who can align stakeholders across federated data domains.
  • Collaborative, pragmatic, and capable of mentoring other architects and engineers.
  • Comfortable operating across multiple business units and global teams.
  • Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.
  • Advanced technical certifications preferred.
  • Hands-on exposure or background in machine learning, AI model lifecycle management, or MLOps frameworks (e.g., SageMaker, Azure ML, MLflow).
  • Experience in investment management, asset management, or financial services.
  • Familiarity with AI/ML architectural patterns, feature stores, and model lifecycle integration.
  • Exposure to Python, Spark, or Databricks for data transformation and ML pipelines.
  • Experience with data observability tools (e.g., Monte Carlo, Great Expectations, Datafold).
  • Certifications in Snowflake, Databricks, or Cloud Data Architecture a plus.
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