Sr Data Architect - Patient Services (Life Sciences)

Tiger Analytics

  • Boston, MA
  • 30+ days ago
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    Skills

    • Artificial Intelligence (AI)unmatched
    • Biologyunmatched
    • Channel Strategiesunmatched
    • Data Analysisunmatched
    • Data Collectionunmatched
    • Data Modelingunmatched
    • Data Qualityunmatched
    • Data Structuresunmatched
    • DataArchitect Data Modeling Toolunmatched
    • Ecosystemsunmatched
    • Master Data Management (MDM)unmatched
    • Patient Careunmatched
    • Problem Solving Skillsunmatched
    • Root Cause Analysisunmatched
    • Service Level Agreement (SLA)unmatched
    • Stewardshipunmatched

    Description

    Tiger Analytics is pioneering what AI and analytics can do to solve some of the toughest problems faced by organizations globally. We develop bespoke solutions powered by data and technology for several Fortune 100 companies. We have offices in multiple cities across the US, UK, India, and Singapore, and a substantial remote global workforce.

    We are seeking a Data Architect - Patient Services to join our Life Sciences practice. In this role, you will be responsible for ensuring the integrity and reliability of Patient Services (PS) data and be the primary bridge between external Patient Services Hub vendors and internal enterprise systems. This is a high-impact role combining technical data modeling with operational stewardship, requiring you to drive data quality, investigate complex lineage issues, and ensure that patient data is "analytics-ready" for strategic decision-making.

    Responsibilities:

    • Architecture & Modeling: Lead source-to-target mapping from external Hub vendors to internal systems (EDB). Define logical/conceptual data models and act as the primary SME for Patient Services data structures.
    • Data Stewardship: Partner with MDM teams to ensure accurate HCO mastering and hierarchy aggregation. Identify data gaps and design solutions to roll up Patient Access data across therapeutic areas.
    • Quality & Integrity: Proactively resolve data issues and define validation rules. Collaborate with DD&T to implement data quality checks within the pipeline ecosystem.
    • Vendor Accountability: Direct engagement with PS Hub vendors and aggregators to investigate root causes, enforce Data SLAs, and validate data fixes before and after transmission.
    • Analytics Enablement: Partner with Analytics & Insights (A&I) to build "Analytics Ready Data" (ARD) layers, ensuring data is reliable, actionable, and compliant with audit standards.

    Numbers & Facts

    LocationBoston, MA

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