Data Architect – Sales & Marketing Technology

Role Overview

The Data Architect designs and implements the conceptual, logical, and physical data architecture within Snowflake that captures the full breadth of Nuveen's sales and marketing operations, including intermediary channel performance, institutional relationships, territory management, product flows across asset classes, AUM and flows analytics, marketing engagement, and client journey data. This role requires a deep understanding of cloud data architecture principles, modern data product design, and the nuances of asset management distribution.

• Owns the end-to-end data architecture of the Snowflake platform, including conceptual, logical, and physical data models that represent asset management sales and marketing operations across intermediary and institutional channels, product hierarchies, territory and coverage structures, AUM and flows, and client/prospect engagement.
• Designs, builds, and curates reusable data products within Snowflake that serve as authoritative sources for downstream systems such as Salesforce, Sitecore, and Tableau, ensuring contracts, SLAs, and consumption patterns are well defined.
• Establishes and enforces data governance standards on the Snowflake platform, including domain ownership, naming conventions, metadata, lineage, access controls, masking, row‑level security, and compliance with regulatory requirements.
• Collaborates with sales, marketing, distribution strategy, and product stakeholders to gather requirements and translate them into scalable data models and data products that support channel analytics and institutional analytics.
• Partners with data engineering teams on ingestion patterns, transformation frameworks, orchestration, and CI/CD practices to ensure efficient, well‑tested, and observable data pipelines feeding Snowflake.
• Develops and maintains dimensional models, star and snowflake schemas, data vault structures, and semantic layers that power business intelligence, personalization, and operational use cases.
• Supports integration of data from multiple structured and unstructured sources, including third‑party intermediaries, institutional data sources, market and benchmark data, CRM, marketing automation, and digital engagement platforms.
• Conducts data profiling and quality analysis to identify gaps, redundancies, and integrity issues, driving remediation through model and pipeline improvements.
• Monitors and tunes Snowflake for performance, scalability, and cost efficiency, applying best practices around warehouse sizing, clustering, materialized views, caching, and workload isolation.
• Communicates data architecture designs, trade‑offs, and roadmaps to technical and non‑technical audiences, including senior leadership and cross‑functional partners.
• Provides guidance and mentorship to data modelers, engineers, and analysts on cloud data architecture and asset management domain concepts.

• University degree (preferred).
• 5+ years of experience in data architecture, modeling, or related discipline; 3+ years directly supporting asset management, wealth management, or broader financial services distribution.
• Hands‑on experience architecting solutions on Snowflake, including security frameworks, data sharing, Snowpark, streams, tasks, performance tuning, and cost governance.
• Proficiency in conceptual, logical, physical data modeling techniques including dimensional modeling, entity‑relationship modeling, and data vault methodologies.
• Experience designing and operating data products with clear ownership, contracts, documentation, and consumer SLAs.
• Strong understanding of the asset management industry across intermediary and institutional channels, including AUM/flows analytics, sales crediting, territory models, and product structures.

• Strong SQL skills.
• Experience with data modeling tools (Erwin, SqlDBM, or similar).
• Experience with transformation frameworks such as dbt.
• Experience with orchestration tools (Airflow, Matillion, or equivalent).
• Familiarity with data governance frameworks, data quality management, and metadata/catalog tools such as Collibra, Alation, or Atlan.
• Strong knowledge of data…