Data Solution Architect - USBU

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Job Description

Role Summary

Responsible for designing and enabling the foundational components of the USBU data and analytics ecosystem—ensuring trusted, governed, high‑fidelity data assets that empower commercial operations, patient services, field teams, and advanced analytics initiatives. This role integrates enterprise data architecture with strong AI/ML, GenAI, Databricks, Tableau, and Informatica capabilities to support a modern, insight‑driven USBU.

How You Will Contribute

Data Architecture & Strategy

• Develop an USBU‑aligned data architecture strategy spanning commercial, PSP, market access, field force, and digital engagement data.
• Lead modernization of data flows across Databricks Lakehouse, Salesforce data models, Tableau semantic layers, and downstream analytics ecosystems.
• Champion architectures that enable AI‑powered decisioning, predictive modeling, and GenAI‑assisted insights.

Leadership, Innovation & AI Adoption

• Provide strategic technical leadership on enterprise AI/ML platforms, aligning capabilities with business needs and emerging GenAI technologies.
• Guide teams in adopting Databricks MLRuntime, Feature Store, Vector Search, Unity Catalog AI governance, and LLM‑powered pipelines.
• Anticipate disruptive AI trends—LLMs, retrieval‑augmented generation (RAG), and autonomous agents—and translate them into actionable architectures for USBU.

Metadata, Governance & Responsible AI

• Establish metadata standards that capture AI lineage, training data provenance, model inputs/outputs, and model governance controls.
• Ensure all AI data assets and models meet organizational expectations for quality, reproducibility, explainability, and compliance.
• Manage and approve USBU Critical Data Elements (CDEs), including those essential for AI‑driven commercial analytics (e.g., HCP segmentation, patient journeys, account hierarchies).

Enterprise Data Assets & AI‑Ready Information Architecture

• Identify enterprise‑significant data assets and design structures that support AI‑ready datasets, training corpora, and feature pipelines.
• Drive architectural alignment from Databricks → Salesforce → Tableau → AI/ML workflows, ensuring interoperability and governance.
• Enable feature engineering, feature reuse, and data quality pipelines needed for scalable machine learning and GenAI workloads.

Advanced Analytics, BI, AI & Insights Enablement

• Architect and expand the USBU’s advanced analytics, ML, and GenAI capabilities, partnering with analytics COEs, commercial leads, and digital teams.
• Support Tableau and CRM users with AI‑enhanced semantic layers, LLM‑powered analytics assistance, and automated insight generation.
• Enable self‑service analytics infused with AI‑generated data stories, anomaly detection, predictive insights, and automated KPI commentary.

Skills & Qualifications

AI, ML & GenAI Skills

• Strong understanding of AI/ML lifecycle, including feature engineering, training, evaluation, drift monitoring, and responsible‑AI controls.
• Experience enabling GenAI use cases such as summarization, segmentation, intelligent automation, RAG, and conversational analytics.
• Deep familiarity with Databricks AI/ML ecosystem, including MLRuntime, Feature Store, AutoML, Vector Search, Unity Catalog AI governance.
• Ability to partner with business teams to translate commercial use cases into machine learning and GenAI solutions.

Databricks, Salesforce & Tableau Expertise

• Advanced proficiency in Databricks (Delta Lake, Unity Catalog, Spark, ML/AI workloads).
• Strong working knowledge of Informatica, including integration, data governance and catalog tools.
• Advanced Tableau expertise with experience enabling AI‑enhanced dashboards, governed extracts, semantic layers, and enterprise BI patterns.

Data Engineering & Architecture

• Expert SQL and distributed computing skills with ability to optimize complex pipelines.
• Advanced understanding of modern lakehouse architectures and data mesh principles.
• Experience designing AI‑ready architectures, vectorized data pipelines, and model‑serving integrations.

Data Governance & Metadata

• Fully independent in designing data governance frameworks including AI‑specific controls.
• Experienced in managing metadata tools that capture lineage, model metadata, and AI training data history.
• Skilled in defining and managing CDEs, particularly those used in predictive analytics.

Professional Leadership

• Operates independently under general direction while influencing organizational data and AI strategy.
• Collaborates closely with senior USBU leaders and cross‑functional partners.
• Mentors engineering, analytics, and governance teams—including upskilling teams on AI‑related practices.

Locations

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