Sr.Data Modeler

About Convene Inc.

Convene, Inc. is a Tampa based, award-winning technology services organization with offices and resources throughout the US, Mexico, and India. We have successful, referenceable customers, competitive benefits, and high-growth opportunities.

Senior Data Modeler
Purpose of the Position
The Senior Data Modeler will be a key contributor within the Data Architecture Team of the Enterprise Data Solutions Department, responsible for designing and governing the Silver layer of the enterprise data lakehouse built on Medallion Architecture. This role translates data from operational systems into standardized, integrated, and analytics-ready enterprise entities, using normalized Third Normal Form (3NF) as the primary modeling approach, supplemented by Data Vault 2.0 and other patterns where appropriate.
The Senior Data Modeler will partner closely with Data Architects, Data Engineers, Business Analysts, Data Governance, and source-system SMEs to ensure models are accurate, traceable, scalable, and implementation-ready, and that they reliably support downstream Gold-layer reporting and analytics.
Primary Duties and Responsibilities

• Silver Layer Data Modeling – Design, develop, and maintain conceptual, logical, and physical data models for the Silver layer of the enterprise data lakehouse built on Medallion Architecture.
• Normalized (3NF) Entity Design – Design 3NF entities for master, reference, transactional, association, episode, and event-history data.
• Data Vault 2.0 Design – Apply hubs, links, satellites, business keys, and historization where appropriate.
• Source Data Profiling – Profile source data to confirm grain, keys, relationships, cardinality, completeness, code domains, and data-quality issues.
• Source-to-Target Mapping – Create attribute-level mappings covering transformations, data types, nullability, key behavior, historization, DQ rules, and PII classification.
• Key Design – Define surrogate keys, alternate keys, source-system crosswalks, and unresolved foreign-key handling.
• Historization – Recommend appropriate patterns, including SCD Type 1, selective SCD Type 2, lifecycle episodes, and append-only events.
• Canonical Modeling – Design reusable canonical models capable of integrating multiple source systems without major redesign.
• Standards & Governance – Define and enforce standards for naming, reference data, lineage, audit, retention, deletion, and data quality.
• Implementation Review – Review implementation SQL and pipelines to ensure alignment with approved models.
• Model Management – Maintain models, definitions, relationships, and documentation in the enterprise modeling tool.
• Gold Layer Support – Support downstream dimensional models and KPI reporting without embedding report-specific logic in Silver.
• Cross-Functional Collaboration – Partner with data architects, data engineers, business analysts, governance teams, and source-system SMEs.

• Required – Bachelor's degree in Information Systems, Computer Science, or a related field.
• Required – 5–10 years of experience in data modeling, data architecture, or data engineering.
• Required – Minimum 2–3 years of hands-on experience with Medallion Architecture (Bronze / Silver / Gold).
• Required – Strong expertise in Third Normal Form (3NF) and normalized relational modeling.
• Required – Strong working knowledge of Data Vault 2.0.
• Required – Advanced SQL skills and experience profiling large operational datasets.
• Required – Experience modeling data for cloud lakehouse or modern data-platform environments.
• Preferred – Experience with SaaS-based data modeling tools such as SQLDBM.

• Architecture: Medallion Architecture, cloud lakehouse design.
• Data Modeling: 3NF, Data Vault 2.0, conceptual/logical/physical modeling, SCD Type 1 and Type 2.
• Keys & Integrity: Business, surrogate, and alternate keys; referential integrity.
• Entity Types: Master, reference, transaction, bridge, association, episode, and event entities.
• Data Management: Source profiling, source-to-target mapping, data lineage, DQ rules.
• Tools: Advanced SQL.
• Collaboration: Strong documentation, communication, and stakeholder management; ability to distinguish source-system behavior from confirmed business meaning.

• Tools: SaaS-based modeling tools such as SQLDBM; ERwin or ER/Studio also considered.
• Platforms: Databricks, Delta Lake, Spark SQL, Unity Catalog.
• Source Systems: ERP, CRM, SIS, LMS, or financial systems integration.
• Governance: Metadata management, PII protection, data-quality frameworks.
• Processing: CDC, schema evolution, retention policies, incremental processing.
• Downstream: Gold-layer dimensional models and enterprise KPI reporting.

• Approved conceptual, logical, and physical Silver-layer models.
• Entity inventory and relationship matrices.
• Attribute-level source-to-target mappings.
• Source-data profiling findings and exception documentation.
• Key, historization, retention, lineage, and DQ specifications.
• Implementation-ready models and documentation supporting review, traceability, and future source integration.

• Silver entities have clearly defined and tested grains.
• Source and business keys are supported by profiling evidence.
• Relationships and optionality are documented and validated.
• Historical behavior is handled consistently and intentionally.
• Models can integrate additional source systems without major redesign.
• Data engineers can implement models without inventing missing business or technical rules.