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Databricks Architect Jobs in Washington (NOW HIRING)

PamTen Inc

Databricks Architect

Washington, DC · On-site

$73 - $96/hr

PamTen Inc is seeking a Databricks Architect to design and implement enterprise-scale solutions on the Databricks Lakehouse Platform. The role involves architecting data pipelines, developing data ...

Mindlance

Databricks Architect

Washington, DC · On-site

$71.25 - $94/hr

Architect Standard III Duration: 12 Months - Long Term Location: Washington, DC 20433 Hybrid Onsite: 4 days per week from Day 1, with a full transition to 100% onsite anticipated soon. * BACKGROUND ...

eSolutionsFirst, LLC

Databricks Solutions Architect

Mclean, VA · On-site

$66.50 - $87.25/hr

Databricks Solutions Architect (RSP) 12 Months contract extension Vienna, VA About the Role * We are seeking an experienced Databricks Resident Solutions Architect (RSA) to lead the architecture ...

New

eSolutionsFirst

Data Solutions Architect-Databricks

Vienna, VA · On-site

$64 - $82.25/hr

... Architect to join their Professional Services team. The role involves working with clients to address big data challenges using the Databricks platform, focusing on data engineering, data science ...

ECS

Senior Databricks Platform Engineer

Quantico, VA · On-site

$140K - $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 ...

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Databricks Architect information

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.

What are the key skills and qualifications needed to thrive as a Databricks Architect?

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 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 most commonly searched types of Databricks Architect jobs in Washington?

The most popular types of Databricks Architect jobs in Washington are:

What are popular job titles related to Databricks Architect jobs in Washington?

For Databricks Architect jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Databricks Architect jobs in Washington look for?

The top searched job categories for Databricks Architect jobs in Washington are:

What cities in Washington are hiring for Databricks Architect jobs?

Cities in Washington with the most Databricks Architect job openings:

Databricks Architect jobs near you
Infographic showing various Databricks Architect job openings in Washington as of August 2026, with employment types broken down into 34% Full Time, and 66% Contract. Highlights an 76% In-person, and 24% Remote job distribution.

Databricks Architect

Washington, DC • On-site

PamTen Inc
IT Services • 51 - 200 employees
Learn more about PamTen Incexternal

$73 - $96/hr

Full-time

Re-posted 14 days ago

Job description

Job Summary:
PamTen Inc is seeking a Databricks Architect to design and implement enterprise-scale solutions on the Databricks Lakehouse Platform. The role involves architecting data pipelines, developing data frameworks, and leading cloud-native implementations while collaborating with business users to deliver scalable data and AI solutions.
Responsibilities:
• Design and implement enterprise-scale solutions on the Databricks Lakehouse Platform.
• Architect end-to-end data pipelines for batch and real-time processing using Apache Spark and PySpark.
• Develop scalable data ingestion, transformation, and data quality frameworks.
• Design and implement Medallion Architecture (Bronze, Silver, Gold) using Delta Lake.
• Build and optimize data warehouses, data marts, and analytical solutions.
• Implement data governance, security, lineage, and access controls using Unity Catalog.
• Develop and support AI/BI dashboards, semantic models, and self-service analytics solutions.
• Configure and optimize Genie Spaces to enable natural language business queries and conversational analytics.
• Design and deploy Generative AI and RAG-based solutions using Databricks Mosaic AI and Vector Search.
• Collaborate with business users to translate requirements into scalable data and AI solutions.
• Optimize Databricks workloads for performance, scalability, reliability, and cost efficiency.
• Lead cloud-native implementations across Azure environments.
• Define architecture standards, best practices, and reusable design patterns.
• Mentor data engineers, analysts, and architects on Databricks technologies and platform adoption.
• Lead migration initiatives from legacy data warehouses and analytics platforms to Databricks.
• Build and maintain Genie Spaces for business self-service analytics.
• Create semantic models, metrics, and trusted data assets for AI-driven reporting.
• Develop natural language-to-SQL analytics solutions using Databricks Genie.
• Implement RAG solutions using enterprise data and Vector Search.
• Optimize AI/BI dashboards and conversational analytics experiences.
• Troubleshoot Spark performance, query optimization, and workload management.
• Automate data validation, monitoring, and governance controls.
• Support AI use cases using Mosaic AI model serving and inference endpoints.
Qualifications:
Required:
• 12 YRS Exp
• Design and implement enterprise-scale solutions on the Databricks Lakehouse Platform.
• Architect end-to-end data pipelines for batch and real-time processing using Apache Spark and PySpark.
• Develop scalable data ingestion, transformation, and data quality frameworks.
• Design and implement Medallion Architecture (Bronze, Silver, Gold) using Delta Lake.
• Build and optimize data warehouses, data marts, and analytical solutions.
• Implement data governance, security, lineage, and access controls using Unity Catalog.
• Develop and support AI/BI dashboards, semantic models, and self-service analytics solutions.
• Configure and optimize Genie Spaces to enable natural language business queries and conversational analytics.
• Design and deploy Generative AI and RAG-based solutions using Databricks Mosaic AI and Vector Search.
• Collaborate with business users to translate requirements into scalable data and AI solutions.
• Optimize Databricks workloads for performance, scalability, reliability, and cost efficiency.
• Lead cloud-native implementations across Azure environments.
• Define architecture standards, best practices, and reusable design patterns.
• Mentor data engineers, analysts, and architects on Databricks technologies and platform adoption.
• Lead migration initiatives from legacy data warehouses and analytics platforms to Databricks.
• Build and maintain Genie Spaces for business self-service analytics.
• Create semantic models, metrics, and trusted data assets for AI-driven reporting.
• Develop natural language-to-SQL analytics solutions using Databricks Genie.
• Implement RAG solutions using enterprise data and Vector Search.
• Optimize AI/BI dashboards and conversational analytics experiences.
• Troubleshoot Spark performance, query optimization, and workload management.
• Automate data validation, monitoring, and governance controls.
• Support AI use cases using Mosaic AI model serving and inference endpoints.
• Databricks Lakehouse Platform
• Apache Spark, PySpark, Spark SQL
• Python, SQL
• Delta Lake, Delta Live Tables, Lakeflow
• Unity Catalog
• Databricks AI/BI and Genie
• Mosaic AI, Vector Search, RAG
• Data Modeling (Dimensional & Data Vault)
• Structured Streaming
• Data Quality and Data Governance
• Azure
• Terraform, Git, Azure DevOps, Jenkins
• REST APIs and Data Integration
• Performance Tuning and Cost Optimization
• Azure Databricks certified Data Eng professional
Company:
PAMTEN is established in the year 2007 as a technology services company, and a Women Owned Business Enterprise registered in New Jersey. Founded in 2007, the company is headquartered in Princeton, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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