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# Databricks Engineer with Test Data Management

- **Company:** Neural Strategic Solutions, Inc.
- **Location:** Chicago, IL, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Microsoft Azure, Continuous Integration, Data Architecture, Data Discovery, Information Engineering, Data Governance, Data Infrastructure, Data Masking, DevOps, Github, Python (Programming Language), Meta-Data Management, Role-Based Access Control, DataOps, Test Data, Data Classification, Generative AI, Data Lakes, Pyspark, Data Management, Terraform, Databricks, Data Generation
- **Published:** November 13, 2026
- **Apply:** https://www.dice.com/job-detail/a6de8200-20c3-41ff-b0be-fc72f3ac4d22

## About the Role

* 8+ years in Data Engineering, Data Architecture, or Data Platform roles
 * 3+ years hands-on Databricks (Lakehouse, Delta Lake, Unity Catalog, PySpark)
 * Medallion Architecture implementation (Bronze/Silver/Gold)
 * Unity Catalog / data governance rollout experience
 * Enterprise data governance - Role based access control, metadata management, data classification, lineage, access control
 * Scalable ingestion, transformation, and provisioning pipelines
 * Azure (ADF, DevOps, Synapse) and/or AWS (S3, Lambda, Glue, Redshift, MWAA)
 * Python and PySpark development

Highly Desired

 * Test Data Management (data masking, de-identification, synthetic data generation)
 * AI-powered data discovery and classification
 * Generative AI / Databricks Mosaic AI
 * Data observability and monitoring frameworks
 * Cross-team framework rollout and enablement

Nice to Have

 * CI/CD and IaC (Terraform, Azure DevOps, GitHub Actions)
 * Regulatory compliance (PII, GDPR, CCPA, HIPAA)

## Description

* Roll out TDM framework across projects on Databricks
 * Establish governance controls for sensitive data in lower environments
 * Build scalable pipelines for test data generation, masking, and refresh
 * Implement metadata management and lineage capabilities
 * Design synthetic data strategies for non-production environments
 * Enable self-service access to compliant test datasets
 * Develop reusable, automated data provisioning and lifecycle frameworks
 * Partner with QA, application teams, data governance, and security