Architect – Data Modeller

Quantiphi

Data Modeler responsible for structural design of canonical data assets for healthcare data integration at Quantiphi. Leading design specification and collaboration with cross-functional teams.

Posted 6/27/2026full-timeMumbai • 🇮🇳 IndiaMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

CloudGoogle Cloud PlatformSQLVault

About the role

Key responsibilities & impact

The Data Modeler is responsible for the structural design of the platform's canonical and derived data assets.
Own the schema-level design of the Common Data Model — DIM (patient, person, encounter, provider, organization), FACT (observation, diagnosis, procedure, medication administration, claim line, encounter), BRIDGE (relationship-qualifier-aware), and REF (terminology and crosswalk) tables.
Design columns, types, NULL semantics, hash key composition, SCD2 patterns, and partitioning strategies in coordination with the architect.
Develop and maintain Source-to-Target Mappings (STTMs) for every source-system feed.
Design data product schemas — the longitudinal patient mart, population analytics aggregations, risk adjustment marts, HEDIS measure pre-aggregations.
Author and maintain unit specs at the model level under the spec-driven development framework.
Collaborate with data engineers to translate model specs into dbt implementations.
Participate in source-system data profiling — analyze sample data from each source to identify quality issues, edge cases, and modeling implications before specs are finalized.
Define and enforce reference data (REF) management practices — terminology crosswalks (LOINC, SNOMED, ICD-10, RxNorm, CPT).

Requirements

What you’ll need

Bachelor's or Master's degree in Computer Science, Information Systems, or a related quantitative field.
5+ years of data modeling experience for large-scale data warehouses, data lakes, or data platforms.
Demonstrated ability to design conceptual, logical, and physical data models — dimensional modeling (Kimball), data vault, or hybrid patterns.
Strong SQL proficiency.
Experience reading and reviewing dbt SQL models is required; ability to author dbt models is a plus.
Experience modeling for analytical and operational data layers simultaneously — understanding how a normalized canonical model serves both downstream analytics and FHIR API consumers.
Hands-on experience with healthcare data standards — HL7v2 segment-level structure, CCDA document structure, and FHIR R4 resource models.
Familiarity with US Core profiles and FHIR Bundle composition.
Experience producing source-to-target mappings (STTMs) at field-level granularity for multi-source data integration projects
Experience modeling SCD2 patterns and the operational implications of late-arriving data, restatement, and version closure.
Experience with terminology systems used in healthcare — LOINC, SNOMED CT, ICD-10, CPT, RxNorm — and their crosswalk patterns.
Familiarity with Google Cloud Platform data services (Cloud Storage, BigLake, Dataproc) and open table formats. Direct Iceberg experience is a plus.
Strong written communication skills. STTMs and model documentation are read across engineering, QA, clinical, and governance audiences.

Benefits

Comp & perks

Flexible work arrangements
Professional development opportunities

ATS Keywords

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Hard Skills & Tools

Data ModelingSQL ProficiencyDimensional ModelingData VaultSCD2 PatternsSource-to-Target MappingsDbt SQL ModelsHealthcare Data StandardsTerminology SystemsAnalytical Modeling

Soft Skills

Strong Written CommunicationCollaborationAnalytical ThinkingProblem SolvingAttention to Detail