S
Full Time
Any
Job detail
Data Modeler- healthcare domain experience
Saransh INC
Pleasanton, California, United States
Job description
Request ID: 112677-1
Role: Data Modeler
Location: PLEASANTON, CA
Number of Positions: 2
Seeking an experienced Data Modeler with a healthcare background to support Revenue Cycle Analytics initiatives. The resource will partner with business stakeholders, analysts, and data engineering teams to understand reporting requirements, assess source data, and design scalable data models that enable reliable and consistent analytics.
The ideal candidate will bring strong hands-on experience in data modeling, SQL, and data modeling tools such as ERwin, along with a solid understanding of healthcare revenue cycle processes and the ability to translate business requirements into practical technical designs.
Roles and Responsibilities
Partner with Revenue Cycle Analytics stakeholders to gather, clarify, and document business requirements, reporting needs, metrics, and data definitions.
Analyze source systems, datasets, and existing reporting structures to identify data relationships, dependencies, and gaps.
Develop and maintain conceptual, logical, and physical data models using ERwin or similar data modeling tools.
Define entities, attributes, primary and foreign keys, relationships, data granularity, and historical tracking requirements.
Design dimensional data models, including fact and dimension tables, to support reporting and analytical use cases.
Develop source-to-target mappings and document transformation logic, business rules, metadata, and data lineage.
Collaborate with data engineering teams to translate models into implementable data structures and integration workflows.
Use SQL for data profiling, relationship validation, reconciliation, discrepancy analysis, and data quality assessment.
Partner with business stakeholders to establish consistent definitions for revenue cycle metrics across reporting solutions.
Support implementation validation and UAT to ensure delivered datasets meet business and reporting requirements.
Maintain data models, data dictionaries, mapping specifications, and knowledge-transfer documentation.
Required Qualifications
Hands-on experience in data modeling for data warehouse, reporting, and analytics solutions.
Experience with ERwin Data Modeler, ER/Studio, or comparable enterprise data modeling tools.
Healthcare domain experience, preferably in Revenue Cycle Analytics.
Strong SQL skills for data profiling, analysis, reconciliation, and validation.
Strong understanding of conceptual, logical, physical, and dimensional data modeling.
Experience with fact/dimension modeling, data relationships, keys, granularity, and historical data management.
Ability to translate business requirements into data models, source-to-target mappings, and technical specifications.
Strong communication skills with experience working directly with business stakeholders, analysts, and engineering teams.
Ability to work independently and deliver within defined timelines.
Role Descriptions: Assess the current legacy data ecosystem including source systems databases data models ETL pipelines reporting dependencies and Alteryx workflows. Inventory and classify existing data assets transformations integrations schedules volumes performance requirements and technical dependencies. Define the Databricks target architecture including data ingestion storage transformation orchestration security governance monitoring and consumption patterns. Develop a migration strategy that distinguishes between: Straightforward lift-and-shift candidates. Workloads requiring refactoring or redesign. Legacy processes that should be retired consolidated or replaced. Translate Alteryx workflows and legacy data-movement logic into scalable Databricks-based patterns using Spark SQL Python notebooks workflows/jobs and related services. Recommend appropriate lakehouse design patterns including medallion architecture Delta Lake tables data quality controls metadata lineage and access-control approaches. Lead technical workshops with business data engineering analytics and platform stakeholders to validate current-state findings and future-state recommendations. Produce actionable technical artifacts including current-state assessment target architecture migration-wave plan dependency map effort estimates risks and implementation roadmap. Provide hands-on technical leadership during proof-of-concept pilot and migration delivery activities. Partner with the broader MARS team and Siva T to ensure recommendations are practical sequenced and aligned with delivery priorities.
Essential Skills: Assess the current legacy data ecosystem including source systems databases data models ETL pipelines reporting dependencies and Alteryx workflows. Inventory and classify existing data assets transformations integrations schedules volumes performance requirements and technical dependencies. Define the Databricks target architecture including data ingestion storage transformation orchestration security governance monitoring and consumption patterns. Develop a migration strategy that distinguishes between: Straightforward lift-and-shift candidates. Workloads requiring refactoring or redesign. Legacy processes that should be retired consolidated or replaced. Translate Alteryx workflows and legacy data-movement logic into scalable Databricks-based patterns using Spark SQL Python notebooks workflows/jobs and related services. Recommend appropriate lakehouse design patterns including medallion architecture Delta Lake tables data quality controls metadata lineage and access-control approaches. Lead technical workshops with business data engineering analytics and platform stakeholders to validate current-state findings and future-state recommendations. Produce actionable technical artifacts including current-state assessment target architecture migration-wave plan dependency map effort estimates risks and implementation roadmap. Provide hands-on technical leadership during proof-of-concept pilot and migration delivery activities. Partner with the broader MARS team and Siva T to ensure recommendations are practical sequenced and aligned with delivery priorities.
Desirable Skills: Azure data factory Databricks Azure Synapse Analytics Python Sql Pyspark Databricks
Keyword: Azure data factory Databricks Azure Synapse Analytics Python Sql Pyspark Databricks
Skills: Data Architecture and Modeling
Experience Required: 10 & Above![]()
Role: Data Modeler
Location: PLEASANTON, CA
Number of Positions: 2
Seeking an experienced Data Modeler with a healthcare background to support Revenue Cycle Analytics initiatives. The resource will partner with business stakeholders, analysts, and data engineering teams to understand reporting requirements, assess source data, and design scalable data models that enable reliable and consistent analytics.
The ideal candidate will bring strong hands-on experience in data modeling, SQL, and data modeling tools such as ERwin, along with a solid understanding of healthcare revenue cycle processes and the ability to translate business requirements into practical technical designs.
Roles and Responsibilities
Partner with Revenue Cycle Analytics stakeholders to gather, clarify, and document business requirements, reporting needs, metrics, and data definitions.
Analyze source systems, datasets, and existing reporting structures to identify data relationships, dependencies, and gaps.
Develop and maintain conceptual, logical, and physical data models using ERwin or similar data modeling tools.
Define entities, attributes, primary and foreign keys, relationships, data granularity, and historical tracking requirements.
Design dimensional data models, including fact and dimension tables, to support reporting and analytical use cases.
Develop source-to-target mappings and document transformation logic, business rules, metadata, and data lineage.
Collaborate with data engineering teams to translate models into implementable data structures and integration workflows.
Use SQL for data profiling, relationship validation, reconciliation, discrepancy analysis, and data quality assessment.
Partner with business stakeholders to establish consistent definitions for revenue cycle metrics across reporting solutions.
Support implementation validation and UAT to ensure delivered datasets meet business and reporting requirements.
Maintain data models, data dictionaries, mapping specifications, and knowledge-transfer documentation.
Required Qualifications
Hands-on experience in data modeling for data warehouse, reporting, and analytics solutions.
Experience with ERwin Data Modeler, ER/Studio, or comparable enterprise data modeling tools.
Healthcare domain experience, preferably in Revenue Cycle Analytics.
Strong SQL skills for data profiling, analysis, reconciliation, and validation.
Strong understanding of conceptual, logical, physical, and dimensional data modeling.
Experience with fact/dimension modeling, data relationships, keys, granularity, and historical data management.
Ability to translate business requirements into data models, source-to-target mappings, and technical specifications.
Strong communication skills with experience working directly with business stakeholders, analysts, and engineering teams.
Ability to work independently and deliver within defined timelines.
Role Descriptions: Assess the current legacy data ecosystem including source systems databases data models ETL pipelines reporting dependencies and Alteryx workflows. Inventory and classify existing data assets transformations integrations schedules volumes performance requirements and technical dependencies. Define the Databricks target architecture including data ingestion storage transformation orchestration security governance monitoring and consumption patterns. Develop a migration strategy that distinguishes between: Straightforward lift-and-shift candidates. Workloads requiring refactoring or redesign. Legacy processes that should be retired consolidated or replaced. Translate Alteryx workflows and legacy data-movement logic into scalable Databricks-based patterns using Spark SQL Python notebooks workflows/jobs and related services. Recommend appropriate lakehouse design patterns including medallion architecture Delta Lake tables data quality controls metadata lineage and access-control approaches. Lead technical workshops with business data engineering analytics and platform stakeholders to validate current-state findings and future-state recommendations. Produce actionable technical artifacts including current-state assessment target architecture migration-wave plan dependency map effort estimates risks and implementation roadmap. Provide hands-on technical leadership during proof-of-concept pilot and migration delivery activities. Partner with the broader MARS team and Siva T to ensure recommendations are practical sequenced and aligned with delivery priorities.
Essential Skills: Assess the current legacy data ecosystem including source systems databases data models ETL pipelines reporting dependencies and Alteryx workflows. Inventory and classify existing data assets transformations integrations schedules volumes performance requirements and technical dependencies. Define the Databricks target architecture including data ingestion storage transformation orchestration security governance monitoring and consumption patterns. Develop a migration strategy that distinguishes between: Straightforward lift-and-shift candidates. Workloads requiring refactoring or redesign. Legacy processes that should be retired consolidated or replaced. Translate Alteryx workflows and legacy data-movement logic into scalable Databricks-based patterns using Spark SQL Python notebooks workflows/jobs and related services. Recommend appropriate lakehouse design patterns including medallion architecture Delta Lake tables data quality controls metadata lineage and access-control approaches. Lead technical workshops with business data engineering analytics and platform stakeholders to validate current-state findings and future-state recommendations. Produce actionable technical artifacts including current-state assessment target architecture migration-wave plan dependency map effort estimates risks and implementation roadmap. Provide hands-on technical leadership during proof-of-concept pilot and migration delivery activities. Partner with the broader MARS team and Siva T to ensure recommendations are practical sequenced and aligned with delivery priorities.
Desirable Skills: Azure data factory Databricks Azure Synapse Analytics Python Sql Pyspark Databricks
Keyword: Azure data factory Databricks Azure Synapse Analytics Python Sql Pyspark Databricks
Skills: Data Architecture and Modeling
Experience Required: 10 & Above