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Quantiphi

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

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

The ideal candidate must demonstrate expertise in data modeling and schema design, with a strong ability to collaborate across teams to ensure data quality and integrity. Proficiency in healthcare data standards and analytical modeling is essential for effective data integration and utilization.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Data ModelingSQL ProficiencyDimensional ModelingData VaultSCD2 PatternsSource-to-Target MappingsDbt SQL ModelsHealthcare Data StandardsTerminology SystemsAnalytical Modeling
Soft Skills
Strong Written CommunicationCollaborationAnalytical ThinkingProblem SolvingAttention to Detail
Tools & Technologies
Google Cloud PlatformCloud StorageBigLakeDataprocDbt
Industry Keywords
Common Data ModelFHIR APIHL7v2CCDAUS Core ProfilesLOINCSNOMED CTICD-10CPTRxNorm

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