Lead Data Engineer

Lead Data Engineer

JOB DESCRIPTION - DB DATA MODELLER

Conceptual | Logical | Physical | Dimensional | Semantic Data Modelling

Role Summary

Seeking a Senior DB Data Modeller with 10+ years of experience translating complex business requirements into scalable, governed and implementation-ready data models. The role will develop conceptual, logical, physical, dimensional, canonical and semantic models for operational, analytical and AI-ready platforms, supported by Insurance and Benefits Management domain experience.

Key Responsibilities

• Create and maintain conceptual, logical and physical data models for transactional databases, data warehouses and Lakehouse platforms.
• Design normalized relational models and dimensional star/snowflake schemas for operational reporting, analytics, BI and AI use cases.
• Define canonical models for benefits, employer, client, plan, member, enrollment, policy, premium, claims, renewal and carrier data.
• Work with business analysts, data owners and architects to identify entities, attributes, relationships, cardinality, keys and business rules.
• Develop source-to-target mappings, transformation rules and model specifications for ETL/ELT and API integration teams.
• Perform data profiling and SQL analysis to validate source structures, model assumptions, referential integrity and data quality.
• Define naming standards, data types, domains, reference data, slowly changing dimensions, effective dating and history patterns.
• Maintain data dictionaries, model metadata, business definitions, lineage and traceability through governed repositories and catalogues.
• Conduct model reviews with architects, DBAs, engineers, BI teams and business stakeholders; resolve duplication and semantic conflicts.
• Support physical implementation, indexing, partitioning, query-performance tuning, model versioning, release impact analysis and production issue resolution.

Mandatory Technical Skills

Conceptual, logical and physical data modelling; relational and dimensional modelling; normalization and denormalization; star and snowflake schemas; entity-relationship modelling; canonical and semantic models; master and reference data; data profiling and source-to-target mapping; advanced SQL; PostgreSQL, SQL Server, Oracle or comparable relational databases; data-warehouse and Lakehouse modelling; Delta Lake and Azure Databricks concepts; Power BI semantic models; metadata, lineage and business glossary; data quality and reconciliation; model versioning and change control; ERwin, ER/Studio, SAP PowerDesigner, Visio or comparable modelling tools; Git, Jira or Azure DevOps; Agile delivery practices.

Experience and Qualifications

• 10+ years of experience in data modelling, database design, data warehousing, data architecture or enterprise data management.
• Proven experience delivering conceptual, logical and physical models for complex enterprise and analytical solutions.
• Strong hands-on SQL, data profiling, mapping, dimensional modelling and relational database-design experience.
• Experience collaborating with business, architecture, database, data-engineering, BI, governance and testing teams.
• Experience implementing modelling standards, model governance, documentation and design-review controls.
• Bachelor’s degree in Computer Science, Information Technology, Data Engineering or a related discipline; relevant data-modelling certification is preferred.

Domain Experience

• Insurance or employee-benefits experience across employer, client, plan, member, eligibility, enrollment, policy, carrier, premium, claims and renewal data.
• Understanding of sensitive and regulated information, employer-level isolation, effective dating, auditability and governed metric definitions.
• Experience modelling canonical benefits data, certified metrics and semantic structures for role-based BI or natural-language analytics is advantageous.

Core Competencies

Structured data thinking, analytical problem-solving, business-to-technical translation, model precision, documentation discipline, stakeholder facilitation and the ability to balance consistency, performance, scalability, governance and implementation practicality.