Senior Enterprise Data Architect

Role: Senior Enterprise Data Architect

Location: Nashville, TN

Duration: 6+ months

Azure Databricks | Data Governance | Power BI | AI Enablement

Role focus: End-to-end ownership of secure enterprise data architecture, from source ingestion through Databricks, governance, Power BI, analytics, and AI consumption.

Role Overview

We are looking for a Senior Enterprise Data Architect who can take end-to-end ownership of enterprise data architecture, from source-system integration and ingestion through data engineering, governance, security, analytics, Power BI, and AI/ML consumption.

We need someone who can independently engage with business and technology stakeholders, understand a problem quickly, ask the right questions, translate business requirements into practical architecture, and drive the solution forward with limited direction.

The ideal candidate combines strong hands-on Azure and Databricks architecture experience with excellent communication, business engagement, architecture judgment, and ownership skills.

Key Responsibilities

1. Own End-to-End Data Architecture

Design and guide the complete data journey: Source Systems -> Ingestion -> Databricks/Lakehouse -> Bronze/Silver/Gold -> Governance and Security -> Semantic/Consumption Layer -> Power BI, Analytics, and AI.

Create scalable, reusable architecture patterns rather than one-off solutions.

Translate business requirements into implementable architecture and remain engaged through delivery.

Evaluate architecture decisions for security, scalability, maintainability, performance, and cost.

2. Lead Azure Databricks and Modern Data Platform Architecture

Azure Databricks and Lakehouse architecture

Unity Catalog, Delta Lake, Databricks SQL, and SQL Warehouses

Bronze, Silver, Gold Medallion architecture

Batch, CDC, event, and streaming ingestion patterns

Auto Loader, Structured Streaming, Lakeflow Jobs, and Databricks Workflows

Domain-oriented data products and enterprise semantic/consumption layers

Metadata, lineage, data quality, observability, performance, and cost optimization

3. Design End-to-End Data Ingestion

Evaluate new data sources and determine the appropriate ingestion, transformation, storage, security, governance, and consumption pattern.

Azure Data Factory

APIs, relational databases, SQL, files, and SFTP

SaaS applications and external data providers

Change Data Capture and streaming/event data

Operational and industrial/OT data patterns

Read-only source integration and secure credential management

4. Build Security and Governance into the Architecture

Ensure data remains properly protected from the originating system through every transformation and consumption layer. The architect must be able to have credible architecture conversations with Cybersecurity, IAM, data owners, and business stakeholders.

Microsoft Entra ID and group-based access

Role-based access control and least privilege

Unity Catalog permissions, lineage, and auditability

Row-level security, column-level security, and masking

Sensitive, confidential, HR, and PII data handling

Data classification, ownership, stewardship, and access approvals

Encryption in transit and at rest

Azure Key Vault, service principals, and managed identities

DEV, UAT, PROD, and DR environment separation

Access monitoring, recertification, and traceability across downstream tools

5. Architect the Power BI and Data Consumption Layer

The candidate does not need to be primarily a Power BI developer, but must understand Power BI architecture and security from a data-platform perspective.

Power BI semantic models and governed enterprise datasets

Import versus DirectQuery architecture considerations

Databricks-to-Power BI connectivity and performance patterns

Workspace, app, dataset, and build-access concepts

Row-level security and downstream access enforcement

Self-service analytics within governed boundaries

Reducing uncontrolled direct connections to source systems

Aligning source, Databricks, and Power BI permissions to prevent accidental exposure

6. Partner Directly with Business and Technology Stakeholders

Work directly with HR, Finance, Commercial, Operations, Supply Chain, Cybersecurity, Enterprise Applications, BI/Analytics, Data Engineering, and AI/Data Science teams.

For each use case, independently determine:

The business problem and intended outcome

The required data and system of record

Data ownership, classification, sensitivity, and access needs

Where data should be ingested, processed, stored, and consumed

Whether the solution belongs in the source application, Enterprise Data Hub, or a hybrid model

The appropriate architecture, security, governance, and reusable implementation pattern

Maintain architecture decision records and exceptions, including rationale, ownership, risks, and remediation expectations.

Drive standardization and reuse across teams, reducing one-off pipelines, duplicate datasets, inconsistent Power BI models, and uncontrolled direct source-system connections.

Provide practical coaching and architecture clarification during delivery, helping teams resolve complex issues without taking over the developer role.

Review technical designs and key implementation decisions for alignment with approved architecture, while avoiding unnecessary control or becoming a delivery bottleneck.

Lead or participate in solution-design reviews and architecture reviews before implementation begins, identifying security, scalability, maintainability, integration, and cost risks early.

Guide Power BI developers on governed semantic models, reusable datasets, workspace and app design, RLS/OLS, Build permissions, sharing controls, performance, and secure Databricks consumption.

Guide data engineers and developers on ingestion, transformation, data modeling, Medallion architecture, security, governance, performance, observability, and deployment patterns.

Define, document, maintain, and communicate enterprise data architecture principles, technical standards, reference architectures, reusable patterns, and design guardrails.

Provide hands-on architecture leadership to Data Engineering, Power BI/BI, application development, integration, data science, and vendor delivery teams. The architect is accountable for translating enterprise architecture into practical technical direction that teams can implement consistently.

7. Provide Technical Leadership, Standards, and Delivery Guidance

Leadership and Working Style

Highly self-directed. Comfortable receiving an ambiguous business problem, locating the right stakeholders, gathering information, developing options, and driving the issue toward resolution with limited day-to-day direction.

Proactive. Raises architecture, governance, data quality, integration, security, performance, or cost concerns early and proposes practical solutions.

Strong communicator. Explains complex Azure, Databricks, security, and data concepts in straightforward business language for both technical and non-technical audiences.

Comfortable with ambiguity. Converts loosely defined needs into clear requirements, decisions, architecture, and next steps.

Collaborative without being dependent. Builds strong cross-functional relationships but does not require another architect or leader to participate in every stakeholder conversation.

Outcome-oriented. Measures success by stakeholder alignment and secure, scalable implementation, not by producing diagrams alone. Provides implementation guidance and verifies that delivered solutions remain aligned with approved standards and architecture.

Required Experience and Qualifications

8+ years of overall data and technology experience

4+ years in data architecture, solution architecture, or senior data engineering architecture roles

Strong recent implementation experience with Azure Databricks

Strong experience designing enterprise data platforms on Microsoft Azure

Practical Unity Catalog and Medallion architecture experience

Strong Azure Data Factory and ADLS Gen2 knowledge

Good understanding of Power BI architecture, semantic models, connectivity, and security

Experience designing RBAC, sensitive-data, and enterprise data-governance models

Working knowledge of Microsoft Entra ID, Key Vault, managed identities, and service principals

Experience with Azure DevOps, CI/CD, and environment promotion controls

Experience with metadata, lineage, data quality, stewardship, and auditability

Demonstrated experience working directly with business stakeholders, Cybersecurity/IAM, application owners, and engineering teams

Experience designing data foundations for analytics, data science, AI, and machine-learning workloads

Preferred Qualifications

Databricks Data Engineer, Databricks architecture-related, or relevant Microsoft Azure certification

Experience with SAP data and enterprise application integration

Experience securing HR, payroll, employee, or other PII data

Experience with operational/industrial data, PI Historian, manufacturing, refining, energy, or logistics

Experience supporting structured and unstructured data

Exposure to MLflow, Model Serving, Vector Search, Genie, or AI gateway patterns

Terraform or Infrastructure-as-Code understanding

Experience with domain data products, semantic layers, and federated governance

What We Are Not Looking For

A developer who has been given an Architect title but lacks end-to-end ownership experience

Someone whose contribution is primarily limited to architecture diagrams or documentation

A Databricks specialist who cannot explain upstream ingestion, governance, security, and downstream consumption

Someone who requires complete technical requirements before beginning discovery

Someone who waits for tasks instead of identifying and driving necessary work

Someone who cannot independently lead business stakeholder discussions

Someone who communicates only in highly technical terminology

A data engineer with limited security, governance, or Power BI architecture understanding

Someone who designs a solution and disengages before implementation

What Success Looks Like

The architect should be able to take a requirement such as, HR needs workforce data available for analytics, but the information is sensitive, and independently drive the full discussion and design:

Source system -> Data owner -> Classification -> Required data -> Ingestion -> Databricks domain/layer -> Unity Catalog -> Entra security group -> Masking/RLS -> Governed consumption -> Power BI -> Access approval -> Lineage and audit.

The person should quickly establish credibility with business users and technical teams, reduce dependency on leadership for routine architecture decisions, and help deliver secure, scalable, governed, and reusable data solutions.

Vendor Guidance

Please prioritize candidates who are technically strong and highly business-facing. We need an architect who can independently take ownership of ambiguous data problems, engage the appropriate business, security, application, and technical stakeholders, and drive an end-to-end solution without requiring significant day-to-day direction.

Strong Databricks and Azure skills alone are not sufficient. Communication, initiative, architecture judgment, security and governance understanding, and the ability to translate technical concepts for business stakeholders are critical selection criteria.

This role is expected to operate as an architect and trusted advisor, not primarily as an individual developer.

Commercial Terms

Rate and engagement terms are open for discussion based on the candidate s experience, architecture depth, communication capability, and overall fit. No rate commitment is being made as part of this job description.