Himalayas · Partner listingSenior Data Modeler
Jimmy Technologies
⌖ WorldwideSalary not providedRemoteContract · Not specified
Source: Himalayas ↗
Location & working arrangementWorking arrangement is supplied by the provider. Check the original advert for attendance and travel requirements.
Provider location / applicant eligibility: Worldwide
About the role
Our partner, a Fortune 500 company, is building scalable, analytics-ready data architectures for a client in the healthcare sector.
We're looking for a Senior Data Modeller to design and maintain dimensional data models using Kimball methodology that power reporting and analytics at scale. You'll translate business requirements into well-structured logical and physical models, working closely with data engineers, BI teams, and business stakeholders — with particular emphasis on healthcare Encounters data.
This is a long-term, remote-first contract position with required US working hours overlap (2-6 PM CET).
Responsibilities
Dimensional modeling: design and maintain data models using Kimball methodology (star and snowflake schemas, fact and dimension tables, SCDs).
Healthcare Encounters data: model Encounters data, ensuring accuracy and alignment with business and regulatory requirements.
Advanced SQL: develop and optimize complex queries for data extraction, transformation, and validation.
Implementation: collaborate with data engineering teams to implement models within the data warehouse/lakehouse environment.
Data quality: perform data profiling, quality checks, gap analysis, and troubleshooting across source and target systems.
Documentation: maintain data models, data dictionaries, and mapping specifications.
BI enablement: partner with BI and analytics teams to ensure models support downstream reporting needs.
Work Conditions
Type: Full-time & Long-term contract work
Start Date: ASAP
Location: Remote
US Time Zone Overlap: Required (2 PM - 6 PM CET, ideally until 7 PM CET)
Contract with EU LLC
Requirements
Healthcare industry experience — including hands-on knowledge of Encounters data.
Experience working with claims, member, or provider data in a healthcare payer (health insurer) or provider setting.
Strong Kimball dimensional modeling background — star/snowflake schemas, fact and dimension tables, slowly changing dimensions (SCDs).
Advanced SQL skills — complex query writing, performance tuning, debugging, and data profiling across large datasets.
Proven ability to design logical and physical data models for data warehouses/lakehouses optimized for reporting and analytics.
Excellent problem-solving skills and keen attention to detail.
Ability to collaborate with cross-functional teams and lead technical discussions with data engineers, BI teams, and business stakeholders.
Have a consultancy mindset → always try to find a solution for the client
Highlights
If you are passionate about Data Modeling, SQL, Healthcare data, and building analytics-ready data warehouses, this role with our client offers an exciting opportunity to shape healthcare analytics!
Originally posted on Himalayas
Skills mentioned in the listing
SQLSnowflakeData engineering
Recognised skills are extracted from the supplied description using canonical names and aliases. They are mentions, not verified essential requirements. Check the source advert for the employer’s full requirements and location restrictions.
Why these skills appear
SQL · mentioned
ng accuracy and alignment with business and regulatory requirements. Advanced SQL: develop and optimize complex queries for data extraction, transformation, and validation. Implementation: collaborate with data engin
Snowflake · mentioned
l modeling: design and maintain data models using Kimball methodology (star and snowflake schemas, fact and dimension tables, SCDs). Healthcare Encounters data: model Encounters data, ensuring accuracy and alignment wi
Data engineering · mentioned
extraction, transformation, and validation. Implementation: collaborate with data engineering teams to implement models within the data warehouse/lakehouse environment. Data quality: perform data profiling, quality