Sr. Data Modeler (Tampa)

Role : Sr. Data Modeler

Location : Tampa, FL (Hybrid)

The Senior Database Designer is responsible for building the organizations enterprise data models and database structures. The role is responsible for conceptual, logical, and physical data modeling that supports operational systems, analytical workloads, and harmonized data domains within the enterprise data ecosystem. The position will partner closely with business SMEs, data engineering, governance, and analytics teams to ensure that data structures are documented, standardized, scalable, performant, and aligned to corporate governance policies and integration standards. The successful candidate will bring deep expertise in dimensional and relational modeling, strong proficiency with modern cloud data platforms, and the ability to drive modeling best practices across the organization.

Key Responsibilities

Enterprise Data Modeling and Architecture

Lead the design and delivery of conceptual, logical, and physical data models for enterprise data domains and data products (operational and analytic).

Develop harmonized, reusable, and governed data models that support single-source-of-truth design principles.

Establish and maintain modeling standards, including naming conventions, dimensional modeling patterns, SCD2 strategies, surrogate key methodologies, lineage documentation, and data enrichment frameworks.

Design models to support high-volume incremental ingestion (CDC), complex history tracking, and auditable data transformations.

Produce and maintain full metadata and lineage documentation through approved tools (e.g., ER / Studio, Unity Catalog).

Integration, Data Engineering Enablement, and Delivery

Create detailed source-to-target mappings aligned to model definitions and business rules to support data engineering development.

Partner with data pipeline engineering to validate build quality, ensure model fidelity in pipelines, and support UAT and performance testing.

Contribute to database and datamart design for analytics solutions, including fact and dimension architectures, semantic layers, and data consumption optimization.

Performance, Quality, and Governance

Validate data model performance characteristics; recommend indexing, partitioning, and clustering strategies for the data platform.

Collaborate with Data Governance to ensure data definitions, standards, quality rules, and ownership are aligned to enterprise data strategy.

Design models emphasizing security classification, access permissions, compliance obligations, and auditability.

Stakeholder Engagement

Serve as a trusted advisor to product owners, business leaders, and analytics users, translating business requirements into data structures that support meaningful insights.

Communicate tradeoffs and design alternatives when evaluating new use cases or changes to the enterprise model.

Contribute to roadmap planning for enterprise data domains and long-term architectural evolution.

Qualifications

Required

o Bachelors or Masters degree in Computer Science, Information Systems, or a related discipline.

o 7+ years of progressive experience in data modeling, database design, and data architecture.

o Demonstrated expertise with relational and dimensional modeling (3NF and star schema design).

o Proficiency with cloud-based modern data stack environments (Azure preferred; Databricks experience highly valued).

o Strong proficiency with SQL for model validation, profiling, and optimization.

o Experience with data modeling tools such as ER / Studio, ERwin, DB Schema, or equivalent.

o Hands-on experience supporting data warehouses, datamarts, and metadata-driven modeling approaches.

o Experience supporting data ingestion and CDC design patterns and SCD2 data history strategy.

o Strong attention to detail regarding data quality, lineage, governance, and documentation.

o Excellent communication skills with proven ability to clearly articulate design rationale to technical and non-technical audiences.

Preferred

o Experience in the insurance or financial services industry with knowledge of policy, client, and revenue data structures.

o Familiarity with ETL / ELT orchestration tools (Fivetran, Airflow, MuleSoft) and distributed processing frameworks (Spark).

o Experience with semantic modeling layers (e.g., Tableau semantic layer, dbt metrics, or similar).

o Certification in cloud platforms (Azure Data Engineer, AWS Data Analytics, or equivalent).