Data Modeler

InterSystems•Boston, MA

14h•$107,000 - $142,000

About The Position

We are looking for a highly motivated, systems-oriented Lead Data Modeler to own the design, governance, and evolution of our data warehouse at InterSystems. This role sits at the center of our data lake / data warehouse initiative and will be responsible for defining how data is structured, understood, and used across the organization. You will work closely with Data Engineering, Sales Tech, CRM, Marketing Technology, and business stakeholders to translate fragmented legacy CRM and operational systems into a unified, scalable data model. This is a hands-on role requiring strong SQL and data analysis skills, with an emphasis on data modeling, source-to-target mapping, and cross-system reconciliation. You will not primarily build ingestion pipelines, but you will define exactly how data should flow, transform, and be represented - serving as the source of truth for all downstream analytics and applications. In addition to modeling, this role will own core aspects of data governance, user access design, user support, and internal data warehouse administration within InterSystems.

Requirements

5+ years of experience in data modeling, data architecture, or related roles.
Strong SQL skills with the ability to independently analyze complex datasets.
Experience designing conceptual, logical, and physical data models.
Experience working with databases (IRIS, PostgreSQL, or similar).
Strong understanding of data normalization, denormalization, and schema design trade-offs.
Experience defining source-to-target mappings and working with ETL/ELT processes.
Ability to work with ambiguous, messy legacy data and derive structured solutions.
Strong attention to detail and commitment to data accuracy and consistency.

Nice To Haves

Experience working with CRM or customer data platforms.
Familiarity with InterSystems IRIS or similar enterprise data platforms.
Experience supporting data governance, access control, and data quality initiatives.
Proficiency in Python for data analysis or validation workflows.
Experience using AI tools to accelerate data analysis, mapping, and documentation.
Strong communication skills with the ability to work across technical and business teams

Responsibilities

Design and maintain conceptual, logical, and physical data models across the data lake / warehouse.
Define canonical data definitions, relationships, and business logic across fragmented legacy systems.
Build scalable, extensible models that reduce redundancy and support analytics, reporting, and operational workflows.
Establish modeling standards, naming conventions, and documentation practices across the platform.
Continuously refine models as new data sources and use cases emerge.
Analyze legacy CRM and other operational datasets to understand structure, quality, and business meaning.
Define detailed source-to-target mapping logic for ingestion into the data model.
Provide clear, implementation-ready transformation specifications to Data Engineers.
Validate incoming data against expected mappings and ensure alignment with defined models.
Act as the primary owner of how legacy data translates into the unified system.
Partner with Data Engineers to identify and resolve discrepancies across systems and pipelines.
Develop frameworks for data validation, integrity checks, and consistency monitoring.
Investigate edge cases and ambiguous data definitions with SMEs and stakeholders.
Establish processes for ongoing data quality governance as the platform scales.
Maintain and optimize schemas and views within the data warehouse, ensuring high performance and reliability.
Support ETL workflows, data integrity checks, and reporting pipelines.
Manage user access, roles, and data security within the warehouse environment.
Monitor query performance and optimize schemas and indexing strategies.
Maintain clear, up-to-date documentation of schemas, tables, and data flows.
Partner with Sales, CRM, Marketing, and other stakeholders to gather and clarify data requirements.
Translate complex data structures into clear, understandable documentation for non-technical users.
Work closely with Data Engineering to ensure seamless execution of ingestion and transformation logic.
Align with internal teams to standardize definitions and avoid duplication of data efforts.
Own the onboarding of new data sources into the data warehouse platform.
Define scalable patterns for integrating additional systems over time.
Establish and manage user groups, access patterns, and data consumption layers.
Contribute to long-term strategy for enterprise data architecture at InterSystems.
Build a comprehensive understanding of existing CRM and operational data sources.
Develop the initial unified data model across core systems.
Define and document source-to-target mappings for key datasets.
Stand up and organize the data warehouse schema.
Establish user groups, access controls, and initial governance processes.
Partner with Data Engineering to support initial ingestion and validation pipelines.
Identify and resolve major discrepancies across legacy systems.
Expand the data model to incorporate additional business domains and data sources.
Evolve the platform into a scalable, enterprise-grade data lake / warehouse.
Establish robust data governance, documentation, and quality monitoring frameworks.
Enable self-service analytics through well-structured and well-documented datasets.
Drive standardization of data definitions across the organization.
Continuously optimize performance, usability, and scalability of the platform.