dbt Developer (AWS Data Lake Program · Healthcare Domain)

dbt Developer (AWS Data Lake Program · Healthcare Domain)

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.

Important: Candidates must be willing to complete dbt Certification within 1 month of joining. Theoretical knowledge alone will not be sufficient — we require demonstrated production experience across all areas of the dbt ecosystem listed below.

KEY RESPONSIBILITIES

1. 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.

2. 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.

3. 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.

4. 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.

5. 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 new community packages; contribute to internal package development where needed.

6. 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.

7. 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 dbt's built-in DAG and lineage graph.
• Conduct documentation reviews with business stakeholders to ensure accuracy and completeness.

8. 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

• 7+ years of data engineering or analytics engineering experience; 5+ years of hands-on dbt in production.
• Expert-level SQL; advanced query optimization on columnar data warehouses (Snowflake, Redshift, BigQuery).
• Deep hands-on dbt experience: models, tests, snapshots, macros, Jinja templating, packages, semantic layer, and docs.
• Proficiency with dbt-utils, dbt-expectations, and dbt-audit-helper in production.
• Experience designing dbt projects for large-scale, multi-domain data lakes or data warehouses.
• Strong understanding of data modelling: dimensional modelling, Star Schema, Data Vault 2.0, and Medallion architecture.
• Experience with CI/CD for dbt: automated test execution, slim CI (state:modified), and deployment pipelines.
• Proficiency in Python for custom dbt operations, scripting, and integration.
• Familiarity with AWS data services: Redshift, S3, Glue, Athena, or equivalent cloud warehouse.
• Experience with version control (Git) and collaborative development workflows (branching, PRs, code review).
• Strong documentation discipline: column-level docs, model descriptions, and data dictionary maintenance.

GOOD TO HAVE

• Healthcare domain knowledge: claims (837/835), member eligibility, pharmacy (NCPDP), clinical data (FHIR/HL7).
• Experience with dbt Cloud: environments, jobs, scheduler, and dbt Explorer.
• Exposure to MetricFlow and dbt Semantic Layer for self-service BI integration.
• Familiarity with data governance and cataloging tools: Collibra, Alation, or AWS Glue Catalog.
• Knowledge of Apache Iceberg or Delta Lake with dbt adapter support.
• dbt Certification (required within 1 month of joining if not already certified).

EDUCATION

• B.Tech / B.E. / M.Tech in Computer Science, Information Technology, or related field.