Principal Data Architect - Data Platforms

Business Unit

Global Technology

$160,000 USD - $190,000 USD

Specific compensation will be based on candidate’s experience, skills, qualifications, commercial considerations, and other job‑related factors permitted by law. At Russell Investments, salary is just one part of our compensation package. Our total rewards approach includes an annual performance bonus (subject to eligibility criteria) in addition to participation in our competitive benefits programs including healthcare, retirement, vacation, and wellbeing programs.

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 (Dev Ops, 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.
• 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 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.
• 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.
• 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.
• Partne…