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Databricks Engineer Jobs in Washington, DC (NOW HIRING)

Resource Point LLC

Databricks Developer

Bethesda, MD · On-site

Databricks Developer Location: Bethesda, MD Duration: 12 Months We are seeking a highly experienced Data Integration and Business Intelligence (BI) Developer with deep expertise in Databricks, AWS ...

Leidos

Databricks Developer

Ashburn, VA · On-site

$131K - $237K/yr

The Homeland Sector within Leidos is seeking a Databricks Developer to design, develop and architect enterprise reporting solutions for critical systems supporting the Passenger Systems Program ...

Leidos

Databricks Developer

Ashburn, VA · On-site

$131K - $237K/yr

The Homeland Sector within Leidos is seeking a Databricks Developer to design, develop and architect enterprise reporting solutions for critical systems supporting the Passenger Systems Program ...

Deloitte

Databricks Engineering Consultant

Rosslyn, VA · On-site

Work you'll do As a Data Engineer II on the AI & Data team, you will be responsible for: * Designing, developing, testing, and maintaining scalable data pipelines and data products on the Databricks ...

Berkley

Databricks Data Engineer

Manassas, VA · On-site

$114K - $137K/yr

The Databricks Data Engineer will help design, build, deploy, and maintain scalable and production grade data pipelines in modern cloud environments, enabling analytics, AI, ML, and decision ...

Berkley

Databricks Data Engineer

Manassas, VA · On-site

$114K - $137K/yr

The Databricks Data Engineer will help design, build, deploy, and maintain scalable and production grade data pipelines in modern cloud environments, enabling analytics, AI, ML, and decision ...

Berkley

Databricks Data Engineer

Manassas, VA

$114K - $137K/yr

The Databricks Data Engineer will help design, build, deploy, and maintain scalable and production grade data pipelines in modern cloud environments, enabling analytics, AI, ML, and decision ...

eTeam

Databricks Platform Engineer

Washington, DC · Remote

Databricks Platform Engineer Job Type: Contract Duration: 8 Months Location: Remote Role Summary: We are seeking a Databricks Platform Engineer to implement and operationalize core platform ...

Horizon Industries

Databricks Data Engineer

Vienna, VA · On-site

$114K - $138K/yr

Who we are looking for We are seeking a Databricks Data Engineer for a full-time position supporting our various clients We are committed to your growth and success in the IT software development ...

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All JobsDatabricks Engineer JobsDatabricks Engineer Jobs in Washington, DC

Databricks Engineer information

See Washington, DC salary details

$67.1K

$126K

$229.1K

How much do databricks engineer jobs pay per year?

As of Jun 16, 2026, the average yearly pay for databricks engineer in Washington, DC is $125,959.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,800.00 and $149,500.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior engineers in high-demand fields such as software engineering, data engineering, and specialized roles like Databricks Engineers can earn $500,000 or more annually, especially with extensive experience, advanced skills in cloud platforms, and leadership responsibilities. Compensation often includes base salary, bonuses, and stock options, particularly in large tech companies or startups with significant funding.

Is it hard to get hired at Databricks?

Getting hired as a Databricks Engineer can be competitive, as the role requires strong skills in big data technologies, cloud platforms, and programming languages like Python or Scala. Candidates often need relevant experience, certifications, and a solid understanding of data engineering concepts to succeed in the hiring process.

What is the salary of engineer in Databricks?

The salary of a Databricks Engineer typically ranges from $100,000 to $150,000 annually, depending on experience, location, and skill level. Senior roles or those with specialized skills in Spark, cloud platforms, or data engineering may earn higher compensation. Salaries can also vary based on company size and industry demand.

Is Databricks Data Engineer in demand?

Databricks Data Engineers are in high demand due to the increasing adoption of cloud-based data platforms and the need for expertise in big data processing, Spark, and cloud environments. Companies seek professionals skilled in data pipeline development, ETL processes, and cloud tools like AWS or Azure, making this a strong career choice with good job prospects.

What are some common challenges faced by Databricks Engineers when working with large-scale data pipelines?

Databricks Engineers often encounter challenges related to optimizing the performance and reliability of large-scale data pipelines. These can include efficiently managing cluster resources, handling data partitioning to prevent bottlenecks, and troubleshooting job failures due to resource constraints or data quality issues. Collaboration with data scientists, analysts, and DevOps teams is essential to ensure seamless integration and deployment of production workflows. Staying current with evolving Databricks features and best practices also plays a key role in overcoming these challenges.

What is a Databricks Engineer?

A Databricks Engineer is a data engineering professional who specializes in using the Databricks platform to build, manage, and optimize data pipelines and analytics solutions. They work with big data technologies like Apache Spark, Delta Lake, and cloud services to process and analyze large datasets efficiently. Their role often involves developing ETL (extract, transform, load) workflows, setting up data lakes, and ensuring data quality and performance for business intelligence and machine learning applications.

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

To thrive as a Databricks Engineer, you need strong expertise in big data processing, cloud platforms (like AWS or Azure), and proficiency with languages such as Python, SQL, and Scala, often supported by a degree in computer science or a related field. Familiarity with Apache Spark, Databricks Workspace, version control systems like Git, and relevant Databricks certifications are typically required. Strong analytical thinking, collaboration, and effective communication skills help you understand business needs and work seamlessly with data teams. These skills ensure efficient data pipeline development, scalable analytics solutions, and successful integration of Databricks into organizational workflows.
What are popular job titles related to Databricks Engineer jobs in Washington, DC? For Databricks Engineer jobs in Washington, DC, the most frequently searched job titles are:
What job categories do people searching Databricks Engineer jobs in Washington, DC look for? The top searched job categories for Databricks Engineer jobs in Washington, DC are:
Databricks Engineer jobs near you
Infographic showing various Databricks Engineer job openings in Washington, DC as of June 2026, with employment types broken down into 99% Full Time, and 1% Part Time. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $125,959 per year, or $60.6 per hour.
Urgent role-Databricks Engineer[Hybrid]-[education Background]

Urgent role-Databricks Engineer[Hybrid]-[education Background]

SmartIPlace

Adelphi, MD • Remote

Contractor

Posted 23 days ago

Job description

Position: Databricks Engineer

Location: Adelphi, Maryland

Visa: Any Visa except CPT, TN and GC (PP number is mandatory to fetch I94)

Interview: webcam interview

 

Responsibilities:

1. Data & AI Platform Engineering (Databricks-Centric):

• Design, implement, and optimize end-to-end data pipelines on Databricks, following the Medallion Architecture principles.

• Build robust and scalable ETL/ELT pipelines using Apache Spark and Delta Lake to transform raw (bronze) data into trusted curated (silver) and analytics-ready (gold) data layers.

• Operationalize Databricks Workflows for orchestration, dependency management, and pipeline automation.

• Apply schema evolution and data versioning to support agile data development.

 

2. Platform Integration & Data Ingestion:

• Connect and ingest data from enterprise systems such as PeopleSoft, D2L, and Salesforce using APIs, JDBC, or other integration frameworks.

• Implement connectors and ingestion frameworks that accommodate structured, semi-structured, and unstructured data.

• Design standardized data ingestion processes with automated error handling, retries, and alerting.

 

3. Data Quality, Monitoring, and Governance:

• Develop data quality checks, validation rules, and anomaly detection mechanisms to ensure data integrity across all layers.

• Integrate monitoring and observability tools (e.g., Databricks metrics, Grafana) to track ETL performance, latency, and failures.

• Implement Unity Catalog or equivalent tools for centralized metadata management, data lineage, and governance policy enforcement.

4. Security, Privacy, and Compliance:

• Enforce data security best practices including row-level security, encryption at rest/in transit, and fine-grained access control via Unity Catalog.

• Design and implement data masking, tokenization, and anonymization for compliance with privacy regulations (e.g., GDPR, FERPA).

• Work with security teams to audit and certify compliance controls.

 

5. AI/ML-Ready Data Foundation:

• Enable data scientists by delivering high-quality, feature-rich data sets for model training and inference.

• Support AIOps/MLOps lifecycle workflows using MLflow for experiment tracking, model registry, and deployment within Databricks.

• Collaborate with AI/ML teams to create reusable feature stores and training pipelines.

 

6. Cloud Data Architecture and Storage:

• Architect and manage data lakes on Azure Data Lake Storage (ADLS) or Amazon S3, and design ingestion pipelines to feed the bronze layer.

• Build data marts and warehousing solutions using platforms like Databricks.

• Optimize data storage and access patterns for performance and cost-efficiency.

 

7. Documentation & Enablement:

• Maintain technical documentation, architecture diagrams, data dictionaries, and runbooks for all pipelines and components.

• Provide training and enablement sessions to internal stakeholders on the Databricks platform, Medallion Architecture, and data governance practices.

• Conduct code reviews and promote reusable patterns and frameworks across teams.

 

8. Reporting and Accountability:

• Submit a weekly schedule of hours worked and progress reports outlining completed tasks, upcoming plans, and blockers.

• Track deliverables against roadmap milestones and communicate risks or dependencies.

Required Qualifications:

• Hands-on experience with Databricks, Delta Lake, and Apache Spark for large-scale data engineering.

• Deep understanding of ELT pipeline development, orchestration, and monitoring in cloud-native environments.

• Experience implementing Medallion Architecture (Bronze/Silver/Gold) and working with data versioning and schema enforcement in enterprise grade environments.

• Strong proficiency in SQL, Python, or Scala for data transformations and workflow logic.

• Proven experience integrating enterprise platforms (e.g., PeopleSoft, Salesforce, D2L) into centralized data platforms.

• Familiarity with data governance, lineage tracking, and metadata management tools.

Smart-iPlace logo

About Smart-iPlace

Sourced by ZipRecruiter

SMART-iPLACE provides innovative staffing and consulting solutions that help our clients achieve their business objectives. We can understand and support all areas of your IT systems from back-end infrastructure to front-end personal productivity. Our goal is create innovative IT solutions that enable your business to be more agile and competitive.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Irving, TX, US

Year founded

2021

Social media

Facebook

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