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# Senior Databricks Architect

- **Company:** Bonbloc Inc
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $120,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Data Security, Key Management, Machine Learning, Meta-Data Management, Named Entity Recognition, Role-Based Access Control, Data Classification, Apache Spark, Data Lakes, Pyspark, Core Data, Deployment Automation, Data Management, Machine Learning Operations, Databricks, Data Generation
- **Published:** June 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=4dc7d2bd291d3d55

## About the Role

Do you have experience in Spark?, * Solid Databricks implementation expertise (Unity Catalog, Lakehouse, PySpark).

 * Hands-on AI/ML engineering experience (NLP, NER, classification models).
 * Strong background in Test Data Management (TDM) and enterprise encryption strategies.
 * Preferred: Financial services experience or deep understanding of regulatory compliance.

## Description

* Architecture & Security: Design the core framework utilizing Databricks Lakehouse. Fully leverage Unity Catalog for metadata management, audit logging, and RBAC models.
 * AI-Driven Masking: Implement Named Entity Recognition (NER) and classification models for context-aware masking and adaptive algorithms.
 * Synthetic Data Generation: Build an AI engine capable of generating high-volume synthetic datasets and handling complex edge cases while ensuring cross-environment consistency.
 * Core Data Engineering: Develop Delta Lake pipelines, optimize PySpark transformations, and manage workflow orchestration using MLflow.

Technical Focus

 * Databricks Ecosystem: Lakehouse, Unity Catalog, Delta Lake, MLflow.
 * Data Security & Privacy: Format-Preserving Encryption (FPE), Test Data Management (TDM), Secure Key Management.
 * AI/ML: PySpark, NLP/NER modeling, Data Classification.

Phased Deliverables

 * Phase 1 (Assessment): Future-state architecture, gap analysis, and implementation roadmap.
 * Phase 2 (Build): Configured Databricks framework, AI masking engine, synthetic data module, and self-service APIs.
 * Phase 3 & 4 (Deploy & Handover): SIT support, automated deployment to vendor environments, and internal team knowledge transfer.


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