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Databricks Architect Jobs in Virginia (NOW HIRING)

ERT

Senior Databricks Platform Engineer

Arlington, VA

$120K - $165K/yr

In-depth knowledge of Databricks architecture, including workspaces, clusters, storage, notebook development, and automation capabilities. * Deep expertise in Databricks Unity Catalog, workspace ...

Entarian

Senior Databricks Platform Engineer

Arlington, VA

$120K - $165K/yr

In-depth knowledge of Databricks architecture, including workspaces, clusters, storage, notebook development, and automation capabilities. * Deep expertise in Databricks Unity Catalog, workspace ...

Entarian

Senior Databricks Platform Engineer

Arlington, VA · On-site

$120K - $165K/yr

In-depth knowledge of Databricks architecture, including workspaces, clusters, storage, notebook development, and automation capabilities. * Deep expertise in Databricks Unity Catalog, workspace ...

BLN24

Data Lead

Mclean, VA · On-site

Lead the design of a Medallion Architecture (Bronze/Silver/Gold) on Databricks * Architect and maintain Vector Databases * Develop data pipelines and ETL operations * Experience creating ...

BLN24

Data Lead

Mclean, VA · On-site

Lead the design of a Medallion Architecture (Bronze/Silver/Gold) on Databricks * Architect and maintain Vector Databases * Develop data pipelines and ETL operations * Experience creating ...

Purple Drive Technologies

Data Architect

Richmond, VA · On-site

$63 - $81.25/hr

The Enterprise Data Architect is responsible for defining and evolving a modern, Databricks-centric data and AI architecture supporting customer, consumer, manufacturing, and supply chain domains.

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Databricks Architect information

What is the difference between Databricks Architect vs Data Engineer?

AspectDatabricks ArchitectData Engineer
Primary FocusDesigning and implementing data solutions on Databricks platformBuilding, maintaining, and optimizing data pipelines and infrastructure
Skills & CertificationsDatabricks certifications, Spark, cloud platforms (AWS, Azure), SQLSQL, ETL tools, cloud platforms, programming (Python, Scala)
Work EnvironmentData platforms, cloud environments, collaboration with data teamsData pipelines, databases, cloud infrastructure, scripting

While both roles work with data and cloud platforms, a Databricks Architect primarily focuses on designing and implementing data solutions using Databricks, whereas a Data Engineer builds and maintains the data pipelines and infrastructure that support these solutions. The Architect often oversees the technical design, while the Engineer handles the day-to-day pipeline development.

What are the key skills and qualifications needed to thrive as a Databricks Architect, and why are they important?

To thrive as a Databricks Architect, you need strong expertise in big data engineering, cloud platforms (such as Azure or AWS), distributed computing, and proficiency in languages like Python or Scala, typically supported by a relevant degree and cloud certifications. Familiarity with Databricks Workspace, Apache Spark, Delta Lake, and CI/CD tools is crucial for designing and implementing scalable data solutions. Excellent problem-solving, communication, and project management skills set top performers apart by enabling effective collaboration and solution delivery. These competencies are essential for architecting reliable, high-performance data platforms that drive business insights and innovation.

What are some common challenges Databricks Architects face when designing large-scale data solutions?

Databricks Architects often encounter challenges such as optimizing cluster performance for cost and efficiency, ensuring data security and compliance across distributed environments, and integrating Databricks with legacy systems or diverse data sources. They must carefully design data pipelines and workflows to handle large volumes of data without bottlenecks, and also collaborate closely with data engineers, data scientists, and IT teams to align on best practices. Staying updated with evolving Databricks features and cloud platform updates is also essential for success in this dynamic role.

What is a Databricks Architect?

A Databricks Architect is an IT professional who designs, implements, and manages data solutions using the Databricks platform, which is built on Apache Spark. They are responsible for creating scalable data pipelines, optimizing data workflows, and ensuring security and compliance within the cloud environment. Databricks Architects often work closely with data engineers, data scientists, and business stakeholders to deliver robust analytics solutions that drive business insights. Their expertise helps organizations leverage big data technologies efficiently and effectively.
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Databricks Architect jobs near you
Infographic showing various Databricks Architect job openings in Virginia as of June 2026, with employment types broken down into 82% Full Time, 15% Part Time, and 3% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution.

Data Architect-Azure Databricks

Purple Drive Technologies

Richmond, VA • On-site

$63 - $81.25/hr

Full-time

Posted 7 days ago

Job description

Overview:
Job Summary:
The Enterprise Data Architect is responsible for defining and evolving a modern, Databricks-centric data and AI architecture supporting customer, consumer, manufacturing, and supply chain domains. This role focuses on designing scalable, high-performance data and AI platforms that enable advanced analytics, machine learning, and generative AI solutions aligned with business strategy. The architect partners closely with business, analytics, and technology leaders to drive adoption of cloud-native data platforms, accelerate AI innovation, and enable data-driven decision-making across the enterprise.
Role:
  • Define and maintain enterprise data architecture principles, reference architectures, and future-state roadmaps with a strong emphasis on Databricks and AI enablement
  • Design end-to-end data and AI architectures, including data ingestion, lakehouse storage, processing, analytics, machine learning, and generative AI workflows
  • Act as a strategic partner to business, analytics, and IT stakeholders to translate business objectives into scalable Databricks-based data and AI solutions
  • Lead evaluation, selection, and adoption of cloud-based data, analytics, and AI technologies, with Databricks as the core platform
  • Design architectures that support secure, resilient, and high-performance AI and analytics workloads at enterprise scale
  • Identify and implement automation opportunities across data pipelines, ML workflows, and AI production deployments
  • Introduce and apply emerging technologies and innovative architecture patterns to accelerate AI-driven business outcomes
  • Define and implement enterprise AI and advanced analytics architectures using Databricks ML and AI capabilities
  • Hands-on experience with machine learning platforms, MLOps pipelines, feature engineering, and model deployment
  • Strong understanding of Generative AI, Large Language Models (LLMs), vector search, and AI application architectures
  • Apply AI solutions to:
    • Demand planning and forecasting
    • Customer and consumer insights
    • Intelligent manufacturing
    • Supply chain optimization

Required Qualifications:
  • Bachelor's or master's degree in Computer Science, Engineering, or a related field
  • 12-16+ years of experience in enterprise data architecture and large-scale data platforms
  • Deep domain experience in customer, manufacturing, or supply chain data ecosystems
  • Proven ability to lead data and AI architecture initiatives and influence senior technical and business stakeholders
  • Strong communication skills with the ability to articulate complex AI and data concepts to executive leadership
  • Capgemini Architects certification level 3 or above, relevant data architecture certifications, IAF andoror industry certifications such as TOGAF 9 or equivalent.

Technology Stack (Representative)
Cloud Platforms
  • Microsoft Azure

Architecture Patterns
  • Lakehouse Architecture (Databricks-centric)
  • Data Mesh
  • Event-Driven Architecture

Data & Analytics Platforms
  • Databricks (Primary Platform)
  • Snowflake
  • Azure Synapse Analytics

Integration & Streaming
  • Apache Kafka
  • Azure Event Hubs
  • API Management

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