Databricks Lakehouse Architect — Lead Governance (Hybrid)

NTT DATA North America

Atlanta (GA)

Hybrid

USD 96,000 - 110,000

Full time

6 days ago

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Job summary

NTT DATA is seeking a senior Data Architect to lead the design and governance of enterprise Databricks Lakehouse platforms from intake to production. You will collaborate with business stakeholders, data owners, security, and engineering teams to define patterns for Bronze-Silver-Gold architectures and secure data sharing.

The role requires strong leadership, deep cloud expertise across AWS/Azure/GCP, and hands-on experience with Unity Catalog and data-governance.

Qualifications

Minimum 10 years of experience in Data Architecture or enterprise data-platform delivery.
Minimum 5 years of hands-on Databricks and lakehouse architecture, including production-scale implementation.
Deep expertise in Medallion Architecture (Bronze, Silver, Gold), Delta Lake, Spark/PySpark, SQL.
Strong experience with Unity Catalog and enterprise data-governance capabilities including cataloging, metadata management, lineage, data ownership, access governance, and audit controls.
Minimum 5 years of data-governance experience covering data quality, metadata, lineage, stewardship, data ownership, policy enforcement, privacy, security, and compliance controls.
Strong cloud architecture experience with AWS, Azure, and/or GCP, including IAM, networking, encryption, secrets, storage, security controls, and integration with Databricks.
Experience designing secure data-consumption patterns including APIs, data sharing, semantic layers, BI integration, and controlled self-service analytics.

Responsibilities

Lead enterprise Databricks Lakehouse architecture using Medallion Architecture (Bronze, Silver, Gold), establishing scalable, reusable, secure, and governed platform patterns.
Define the target-state Databricks architecture across ingestion, transformation, storage, orchestration, analytics, streaming, data sharing, and consumption layers.
Design and govern Delta Lake / Delta-based data architectures, including data layout, performance optimization, schema evolution, data lifecycle, and workload-management standards.
Own the enterprise data-governance architecture on Databricks, with strong focus on Unity Catalog for centralized cataloging, access control, data discovery, lineage, ownership, and governed data sharing.
Define data domains, catalogs, schemas, ownership models, classification standards, business metadata, technical metadata, and stewardship patterns in partnership with enterprise data-governance teams.
Establish fine-grained security and access-control patterns using RBAC/ABAC principles, IAM integration, least-privilege access, row/column controls, masking, encryption, secure sharing, and auditability.
Architect enterprise data-quality controls and observability, including validation rules, quality metrics, reconciliation, freshness/completeness monitoring, lineage, exception management, and SLA/SLO reporting.
Design high-performance batch and streaming pipelines using Databricks capabilities and distributed processing patterns; establish reusable frameworks, engineering standards, and orchestration practices.
Provide architecture for governed data exposure through APIs, Delta Sharing / secure data sharing, semantic layers, BI tools, ML/AI consumers, and downstream analytical applications.
Translate business and governance requirements into conceptual, logical, and physical data models; define modeling standards for lakehouse, dimensional, and enterprise data structures.
Serve as the first architecture point of contact for new projects and enhancements; lead discovery, feasibility assessment, architecture reviews, requirements clarification, and solution alignment.
Partner with Information Security, Risk, Compliance, Privacy, and Data Governance teams to embed regulatory and enterprise controls into the Databricks platform and data-product lifecycle.
Drive migration and modernization of legacy data platforms and workloads to Databricks, including architecture assessment, workload rationalization, migration patterns, performance tuning, and cutover strategy.
Support production issue analysis and triage during onshore hours, helping resolve data-quality, security, performance, pipeline, and platform issues while minimizing business impact.
Mentor data engineers and architects, provide design oversight and architecture governance, and work with vendors/system integrators to ensure implementation compliance.