Data Modeler

Role and Responsibilities – Data Modeler / Data Product Specialist:

· Partner with business stakeholders, data product owners, data architects, engineers, and analytics teams to understand business processes, data consumption needs, reporting requirements, and analytical use cases.

· Translate business and analytical requirements into scalable conceptual, logical, and physical data models that support governed data products, self-service BI, operational reporting, and advanced analytics.

· Design domain-oriented data products by defining entities, attributes, relationships, hierarchies, grain, metric logic, business rules, and reusable certified datasets.

· Create and maintain enterprise-grade data model artifacts including ER diagrams, dimensional models, source-to-target mappings, data dictionaries, metadata definitions, lineage documentation, and model change logs.

· Develop modeling patterns across normalized, dimensional, data vault, and denormalized structures, selecting the right approach based on business consumption, performance, governance, and platform needs.

· Define data product contracts, data quality expectations, acceptance criteria, ownership boundaries, refresh frequency, security needs, and consumption-ready outputs for downstream users.

· Perform data profiling and relationship analysis to validate source data structures, identify quality gaps, understand cardinality, assess business rule fitment, and recommend remediation actions.

· Collaborate with data engineering teams to ensure models are implemented accurately across warehouse, lakehouse, semantic, and reporting layers while maintaining consistency between design and build.

· Define and govern metric logic, KPI definitions, reusable business rules, data lineage, and traceability so that data products are auditable, explainable, and trusted by business users.

· Support data quality design by specifying validation checks, reconciliation rules, anomaly thresholds, reference data controls, and exception-based monitoring requirements for critical data products.

· Participate in model reviews, design walkthroughs, UAT support, defect triage, and sign-off discussions with business, architecture, governance, and engineering stakeholders.

· Maintain clear documentation of design decisions, assumptions, dependencies,

business definitions, transformation rules, data quality outcomes, and known limitations to support long-term product ownership.

· Work in an Agile delivery model with product owners, scrum masters, program managers, and engineering teams to prioritize backlog items, refine user stories, and deliver iterative data product enhancements.

Candidate Profile:

· Bachelor’s/master’s degree in computer science, engineering, information systems, data management, operations research, statistics, or related analytics areas.

· Strong hands-on experience in conceptual, logical, and physical data modeling for analytics, reporting, data warehouse, lakehouse, or enterprise data platforms.

· Deep understanding of dimensional modeling, entity-relationship modeling, normalization/denormalization, hierarchies, slowly changing dimensions, fact tables, conformed dimensions, and model performance considerations.

· Strong SQL skills with the ability to profile data, analyze source structures, validate relationships, test business rules, and support engineering teams during implementation.

· Experience creating data dictionaries, source-to-target mappings, business glossaries, metadata inventories, lineage documentation, metric definitions, and certified dataset specifications.

· Exposure to modern data platforms and tools such as Azure Data Lake/Databricks/Snowflake/Redshift/SQL Server/SAS/Power BI semantic models, dbt, Erwin, ER/Studio, or similar modeling and cataloging tools.

· Good understanding of data product concepts including ownership, reusable datasets, data contracts, quality SLAs, semantic consistency, governance controls, and consumption-ready design.

· Ability to work with business users to clarify definitions, resolve metric ambiguity, document assumptions, and convert functional requirements into precise data structures.

· Experience supporting data quality, reconciliation, UAT, model validation, and governance review processes across critical data assets.

· Exposure to Agile delivery concepts such as backlog refinement, sprint planning, user story definition, acceptance criteria, and iterative product releases.

· Able to work in a fast-paced and continuously evolving environment with the ability to handle ambiguity, ask the right questions, and drive clarity across business and technical teams.

· Strong written and verbal communication skills with the ability to explain data models, definitions, lineage, and design trade-offs to business and technical stakeholders