ABOUT THE ROLE

We are looking for two hands-on dbt Developers with deep, production-proven experience across the full dbt ecosystem. This is not a querying or reporting role we need engineers who have built and owned dbt projects end-to-end in complex data environments. You will be the primary owner of the transformation layer in our AWS-based Healthcare Data Lake, responsible for modelling, testing, documentation, and semantic layer design across Member, Claims, Provider, and Clinical domains.

KEY RESPONSIBILITIES

Data Modelling

- Design and build modular, layered dbt models across Bronze / Silver / Gold (Medallion) architecture for healthcare domains: Member, Provider, Claims, Clinical, and Pharmacy.
- Apply dimensional modelling principles (Star Schema, Data Vault 2.0) to create analytics-ready data marts.
- Implement incremental materialization strategies (incremental, table, view, ephemeral) to optimize performance and cost.
- Define and enforce naming conventions, folder structure, and model hierarchy standards across the dbt project.
- Collaborate with Data Architects to ensure logical and physical models align with the enterprise data model.

Testing Data Quality

- Implement comprehensive dbt auto-testing frameworks: built-in generic tests (not_null, unique, accepted_values, relationships) and custom singular tests.
- Integrate dbt-expectations for advanced, data-quality assertions (row count checks, column-level distributions, regex patterns, freshness thresholds).
- Define and enforce data SLAs through source freshness tests and automated alerting.
- Build test coverage dashboards and maintain minimum test coverage standards across all models.
- Triage and resolve data quality failures in CI/CD pipelines; document root-cause findings.

Snapshots





- Design and implement dbt Snapshots to track slowly changing dimensions (SCDs) for Member eligibility, Provider rosters, and Plan configurations.
- Define appropriate snapshot strategies (timestamp vs. check) based on source system characteristics.
- Manage snapshot history tables; ensure accurate historization for compliance and audit requirements.
- Validate snapshot integrity across dbt runs; handle late-arriving data and out-of-order records.

Macros Jinja Templating

- Write reusable dbt macros using Jinja2 to encapsulate business logic, reduce code duplication, and enforce consistency across models.
- Build macros for common healthcare patterns: date spine generation, ICD code grouping, member-month calculations, and claim adjudication logic.
- Apply advanced Jinja control structures (loops, conditionals, modules) for dynamic SQL generation.
- Develop and maintain a macro library; document usage, inputs, and expected outputs for each macro.

Packages

- Integrate and manage dbt packages including dbt-utils, dbt-expectations, dbt-audit-helper, and dbt-codegen.
- Use dbt-utils for surrogate key generation, date spine, pivot/unpivot, star schema helpers, and cross-database compatibility.
- Use dbt-audit-helper to compare model outputs between environments and validate refactoring changes.




- Evaluate and onboard recent community packages; contribute to internal package development where needed.

Semantic Layer Metrics

- Define and maintain the dbt Semantic Layer: create MetricFlow metric definitions, dimensions, entities, and measures for self-service analytics.
- Expose consistent, governed metrics (member months, PMPM, claim approval rate, readmission rate) to BI tools via the semantic layer.
- Collaborate with Data Scientists and business stakeholders to align metric definitions with analytical requirements.
- Manage semantic layer versioning and ensure backward compatibility as models evolve.

Data Dictionary Documentation

- Author and maintain comprehensive dbt documentation: model descriptions, column-level definitions, and business context for all production models.
- Build and publish dbt docs sites; enforce documentation standards as part of the PR review process.
- Maintain a Data Dictionary covering all entities, attributes, business rules, and lineage across healthcare domains.
- Produce and maintain data lineage diagrams (source-to-consumption) using dbts built-in DAG and lineage graph.
- Conduct documentation reviews with business stakeholders to ensure accuracy and completeness.

Engineering Collaboration

- Manage dbt project CI/CD: integrate dbt runs and tests into GitHub Actions or Azure DevOps pipelines; enforce PR-level testing gates.
- Perform code reviews; maintain dbt project hygiene (ref() vs. source(), no raw SQL, consistent style).
- Collaborate with Data Engineers on upstream pipeline design; coordinate with Data Scientists and ML Engineers on feature layer delivery.
- Support data consumers (BI, Analytics, ML) by maintaining SLAs and responding to data issues.

REQUIRED SKILLS EXPERIENCE

📌 dbt Developer (India)
🏢 Aventus Informatics
📍 India