We are seeking a Senior Data Modeler to lead the assessment and redesign of a legacy data ecosystem into a modern, scalable data lake architecture.

Requirements

Assess existing data ingestion pipelines, storage structures, data models, and batch processing dependencies
Identify root causes of recurring data quality issues, lineage gaps, and downstream performance bottlenecks
Design a scalable, layered data architecture supporting raw ingestion, standardized data, and curated consumer-aligned datasets
Define canonical data models to standardize heterogeneous partner data across formats
Recommend and apply appropriate modeling approaches, including dimensional, Data Vault, normalized, or hybrid models
Establish data validation, enrichment, and transformation patterns that improve reliability and consistency
Define data quality rules, validation checkpoints, naming conventions, and metadata standards
Design data contracts and schema evolution strategies to support partners onboarding and backward compatibility
Establish governance and ownership models, including data stewardship and accountability frameworks
Define metadata management, lineage tracking, observability, and lifecycle management standards
Conduct structured gap analysis between current and target states
Develop a phased migration roadmap that minimizes operational risk and stabilizes downstream batch ecosystems
Provide implementation recommendations across people, process, tooling, and governance

Benefits

Requirements

Assess existing data ingestion pipelines, storage structures, data models, and batch processing dependencies

Identify root causes of recurring data quality issues, lineage gaps, and downstream performance bottlenecks

Design a scalable, layered data architecture supporting raw ingestion, standardized data, and curated consumer-aligned datasets

Define canonical data models to standardize heterogeneous partner data across formats

Recommend and apply appropriate modeling approaches, including dimensional, Data Vault, normalized, or hybrid models

Establish data validation, enrichment, and transformation patterns that improve reliability and consistency

Define data quality rules, validation checkpoints, naming conventions, and metadata standards

Design data contracts and schema evolution strategies to support partners onboarding and backward compatibility

Establish governance and ownership models, including data stewardship and accountability frameworks

Define metadata management, lineage tracking, observability, and lifecycle management standards

Conduct structured gap analysis between current and target states

Develop a phased migration roadmap that minimizes operational risk and stabilizes downstream batch ecosystems

Provide implementation recommendations across people, process, tooling, and governance