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PCCA · 1 day ago

Data Modeler

Houston, TX

Full-time

Onsite

Mid Level

3+ years exp

PCCA is a company that helps pharmacists and prescribers create personalized medicine. They are seeking a mid-level Data Modeler to design dimensional models and Power BI semantic models to support various healthcare and commercial data analytics initiatives.

Pharmaceutical

Diversity & Inclusion

Responsibilities

Design dimensional models in the Gold layer for PCCA domains: Commercial/Operations: Orders and order lines, inventory availability, shipment/fulfillment, returns, pricing, promotions, member subscriptions. Membership/Education: Membership lifecycle, entitlements, course enrollments and completions (CE), event attendance, certifications. Product/Clinical: Product catalog (APIs, bases, devices), formulations/recipes and versioning, stability/BUD attributes, allergens, SDS/CoA linkage. Compliance/Quality: USP 795/797/800 compliance data, audit findings, corrective actions, sensitivity labeling, support case/ticketing

Define model grain, surrogate keys, conformed dimensions (e.g., Customer/Account, Product, Practitioner, Date, Geography), and role-playing dimensions

Implement SCD Type 1/2 in Delta (Warehouse/Lakehouse) for key dims (Customer, Product, Practitioner, Membership)

Handle late-arriving facts/dimensions, inferred members, and bridge tables for many-to-many (e.g., formulation → multiple actives/allergens)

Build and maintain Power BI semantic models: Clean star schemas, relationship management, hierarchies, display folders. Author DAX measures for revenue, margin, order volume, fill rates/OTIF, inventory days, membership MRR/retention/churn, education completions, compliance KPIs. Implement time intelligence (MTD/QTD/YTD, PY), semi-additive measures (inventory snapshots). Configure incremental refresh, aggregations where needed, and Row-Level Security (e.g., by region/account team)

Optimize performance and cost: Propose partitioning/clustering/Z-ordering strategies in Delta; consider Warehouse materialized views for heavy rollups; reduce cardinality in Power BI; choose pre-aggregation vs. Aggregations in PBI

Collaborate on Medallion data flow: Specify Silver contracts and quality checks (dedupe, CDC alignment, schema expectations). Review Pipelines/Dataflows Gen2/Notebooks outputs for model-readiness and effective dating

Governance and documentation: Maintain data dictionaries, lineage, SCD behavior, and certified metric definitions; apply sensitivity labels and endorsements

Qualification

Data modelingMicrosoft FabricPower BISQLDimensional modelingDAXData qualityGit workflowsTesting/observabilityPerformance tuningHealthcare domainCommunication

Required

3–5 years in data modeling or analytics engineering with strong SQL

Hands-on Microsoft Fabric: Lakehouse/Warehouse, OneLake, Delta tables, Dataflows Gen2 or Pipelines; familiarity with SQL endpoint usage

Dimensional modeling proficiency: Star schemas; fact types (transactional, periodic snapshot, accumulating); bridge tables for M:N; degenerate and junk dimensions

SCD Type 1/2 with MERGE; effective/expiry dating; handling late-arriving data

Power BI semantic modeling and DAX: Clean tabular model design; CALCULATE/KEEPFILTERS/USERELATIONSHIP; date intelligence; semi-additive measures; model properties (data types, sort-by, formatting)

Incremental refresh; basic aggregations; RLS

Data quality and governance: Define tests (unique/not-null/accepted values), document metrics, manage endorsements; apply sensitivity labels for PII/regulated data

Communication: Translate stakeholder requirements into grain/facts/dimensions and certified measures; collaborate across DE, BI, and business teams

Preferred

Healthcare/life sciences or pharmacy domain exposure: Product master for APIs/bases/devices, SDS/CoA linkages, allergens, formulations and versioning, USP compliance artifacts, membership programs and CE tracking

Performance tuning in Fabric: Warehouse materialized views/indexing, Delta OPTIMIZE and Z-ordering, partition pruning; direct lake/direct query/composite model patterns

Testing/observability: Power BI dataset testing patterns; data freshness/volume drift monitoring

Git workflows: Power BI Projects (.pbip), Fabric Git integration, environment-based deployment pipelines

Experience with calculation groups for time intelligence; metric layer governance