Senior Data Analyst

Position Responsibilities

• Develop and maintain logical and physical data models, database designs, and master data standards that ensure data is structured for efficient use across operational and analytical consumers.
• Build and maintain a unified, authoritative source of truth for master data entities by designing governed hub structures, defining survivorship rules, and resolving cross-system discrepancies to eliminate data silos.
• Operationalize critical data elements (CDEs) and product taxonomy by defining attribute-level rules, classification hierarchies, and standardized value sets that ensure consistent field usage across models, pipelines, and reporting layers.
• Design and maintain master data processes—including canonical schemas, API contracts, and ETL/ELT pipeline specifications—that support both real-time operational workflows and batch analytics consumption.
• Maintain a single trusted view of master data—including golden record definitions, match-merge rules, and certified entity attributes—to support reliable decision-making and business growth.
• Diagnose data-related issues—including referential integrity failures, schema drift, duplicate entity proliferation, and null/inconsistency patterns—and recommend targeted solutions to improve data integrity, security, and usability.
• Maintain lineage documentation connecting raw source fields to governed definitions and downstream model consumption; version-control data dictionaries and field-level mapping documents.
• Support data migration and system transition initiatives by assessing legacy data structures, identifying quality gaps, and mapping source fields to target canonical models.
• Work closely with data engineering, data architecture, analytics, and product teams to align master data standards, embed requirements early in the development cycle, and deliver consistent data outputs across all team boundaries.
• Run focused working sessions with EDI leads and subject-matter experts to gather data requirements, validate field definitions, and resolve conflicting interpretations across systems.
• Share clear, concise updates on data model progress, field classification status, and domain coverage with both technical teammates and non-technical stakeholders to maintain shared understanding and trust in data assets.