Data Scientist – Data Mapping & Architecture Support

ECS is seeking a Data Scientist to work remotely. This role bridges data science and data architecture. The person will map data sources and relationships, build and maintain a company-wide data dictionary, and support the Enterprise Architect in applying policy, standards, and business logic to the overall data framework. Key Responsibilities Data Mapping Identify, trace, and document how data flows across systems — from source to destination — including transformations, dependencies, and ownership. Data Dictionary Development Build and maintain a comprehensive data dictionary defining every field, its meaning, format, source, and business context, so teams have one consistent reference. Architecture Support Work closely with the Enterprise Architect to translate data mapping and dictionary findings into the technical architecture, ensuring data models reflect real business logic and policy requirements. Data Quality & Standards Analyze data for consistency, accuracy, and completeness. Flag gaps or conflicts between systems and recommend standardization. Business Logic Validation Partner with business stakeholders (HR, Finance, Operations, etc.) to confirm that documented business rules match how data is actually defined and used. Governance Collaboration Support data governance efforts by aligning the data dictionary and mapping work with company policy, compliance needs, and access control requirements. Documentation & Reporting Create clear, maintainable documentation (data lineage diagrams, dictionary entries, mapping reports) that both technical and non-technical teams can use. Salary Range: $120,000 - $130,000 General Description of Benefits Experience with data mapping and data lineage tools and techniques Strong skills in building and maintaining a data dictionary or metadata repository Solid understanding of data modeling concepts (entities, relationships, schemas) Proficiency in SQL and at least one programming language (Python preferred) Experience working with large, multi-source datasets (databases, APIs, flat files, etc.) Ability to interpret business logic and policy and translate it into clear data definitions Strong attention to detail and documentation skills