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Azure Data Engineer Jobs in Virginia (NOW HIRING)

ADP, Inc.

Data Engineer

Arlington, VA · On-site

$125 - $150/hr

The Data Engineer will work closely with software engineers, the Chief Engineer, product teams ... The ideal candidate brings strong experience with PostgreSQL, Microsoft Azure, data modeling, ETL ...

New

Accenture Federal Services

Data Engineer

Arlington, VA · On-site

$93K - $176K/yr

AWS Glue, Google Cloud Dataflow, Azure Data Factory) * Experience with Cloud data warehousing ... Data engineering certification such as Palantir Foundry Data Engineer, Azure Data Engineer ...

Pioneering Evolution, LLC

Data Engineer

Arlington, VA · On-site

$125 - $150/hr

The Data Engineer will work closely with software engineers, the Chief Engineer, product teams ... The ideal candidate brings strong experience with PostgreSQL, Microsoft Azure, data modeling, ETL ...

CapTech Consulting

Data Engineer (AWS, Azure, GCP)

Reston, VA · On-site

$119K - $143K/yr

Specific responsibilities for the Data Engineer - Cloud position include:  * Developing data pipelines and other data products using Amazon Web Services (AWS), Microsoft Azure, or Google Cloud ...

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All JobsAzure Data Engineer JobsAzure Data Engineer Jobs in Virginia

Azure Data Engineer information

See Virginia salary details

$44.1K

$128.6K

$176K

How much do azure data engineer jobs pay per year?

As of Sep 8, 2026, the average yearly pay for azure data engineer in Virginia is $128,604.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,500.00 and $136,300.00 per year, depending on experience, location, and employer.

What is an Azure Data Engineer?

Azure Data Engineers are IT professionals who design, implement, and manage data solutions using Microsoft Azure cloud services. They are responsible for building data pipelines, integrating diverse data sources, and ensuring data is stored securely and efficiently. These engineers work with tools like Azure Data Factory, Azure Databricks, and Azure Synapse Analytics to process, transform, and analyze large volumes of data. Their main goal is to provide reliable data infrastructure to support business intelligence and analytics needs.

What are the key skills and qualifications needed to thrive as an Azure Data Engineer?

To thrive as an Azure Data Engineer, you need proficiency in data modeling, SQL, ETL processes, and a solid understanding of cloud computing concepts, typically supported by a degree in computer science or a related field. Familiarity with Microsoft Azure services (such as Azure Data Factory, Azure Synapse Analytics, and Azure Databricks), and relevant certifications like Microsoft Certified: Azure Data Engineer Associate, are highly valuable. Strong problem-solving skills, effective communication, and adaptability help you collaborate across teams and respond to evolving project needs. These skills are crucial for designing robust data solutions that support business intelligence and decision-making in cloud environments.

What are some common challenges Azure Data Engineers face when integrating data from multiple sources?

Azure Data Engineers often encounter challenges when consolidating data from diverse sources such as on-premises databases, cloud storage, and third-party applications. Issues like data format inconsistencies, varying data quality, and synchronization timing can complicate the integration process. Leveraging Azure services like Data Factory and Synapse Analytics helps automate and streamline these tasks, but careful planning and robust data validation are essential. Collaboration with business analysts and data architects is also crucial to ensure the integrated data meets organizational requirements.

What is the difference between Azure Data Engineer vs Data Analyst?

AspectAzure Data EngineerData Analyst
Required CredentialsAzure certifications, SQL, Python, cloud skillsData analysis certifications, SQL, Excel, BI tools
Work EnvironmentCloud platforms, data pipelines, big data toolsData visualization, reporting, business insights
Industry UsageTech, finance, healthcare, retailMarketing, finance, healthcare, retail

Azure Data Engineers focus on building and maintaining data pipelines in cloud environments, utilizing tools like Azure Data Factory and SQL. Data Analysts interpret data to generate reports and insights, often using Excel and BI tools. While both roles work with data, Azure Data Engineers handle data infrastructure, whereas Data Analysts focus on data interpretation and visualization.

Is an Azure Data Engineer a good career?

An Azure Data Engineer is a valuable role focused on designing and implementing data solutions using Microsoft Azure cloud services. It typically requires skills in data modeling, SQL, and tools like Azure Data Factory and Databricks, with certifications such as Microsoft Certified: Azure Data Engineer Associate enhancing job prospects. The role offers strong demand due to the increasing reliance on cloud-based data infrastructure across industries.

What are the most commonly searched types of Azure Data Engineer jobs in Virginia?

The most popular types of Azure Data Engineer jobs in Virginia are:

What are popular job titles related to Azure Data Engineer jobs in Virginia?

For Azure Data Engineer jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Azure Data Engineer jobs in Virginia look for?

The top searched job categories for Azure Data Engineer jobs in Virginia are:

What cities in Virginia are hiring for Azure Data Engineer jobs?

Cities in Virginia with the most Azure Data Engineer job openings:

Azure Data Engineer jobs near you
Infographic showing various Azure Data Engineer job openings in Virginia as of August 2026, with employment types broken down into 89% Full Time, and 11% Contract. Highlights an 85% In-person, 4% Hybrid, and 11% Remote job distribution, with an average salary of $128,604 per year, or $61.8 per hour.

Principal Data Engineer

Jobtailor

Vienna, VA • On-site

$150 - $200/hr

Other

Posted 3 days ago

New

Job description

  • Provide technical leadership and hands‑on engineering expertise for Enterprise Data and Analytics Modernization initiatives across data engineering, analytics, reporting, and data consumption
  • Design, develop, and optimize scalable batch, near real‑time, and real‑time data pipelines using Spark, Python, SQL, Databricks, Microsoft Fabric, Azure Data Factory, and related Azure cloud services
  • Modernize legacy Analytical Data Store and BI workloads by migrating fragmented reporting and data assets into secure, standardized, governed, and cloud‑native analytics platforms
  • Lead API‑driven and event‑based data onboarding patterns using Gravitee, MuleSoft, Kafka, and related technologies
  • Establish reusable engineering ETL frameworks, design patterns, and automation for ingestion, transformation, validation, reconciliation, monitoring, and production support
  • Implement automated data quality validation, reconciliation controls, exception handling, alerting, monitoring, SLA management, and production readiness practices
  • Enable metadata management, lineage, cataloging, and access governance through Unity Catalog, Alation, and related governance capabilities
  • Partner with data architects, analysts, product owners, business stakeholders, governance teams, and platform teams to translate business needs into scalable technical solutions
  • Guide engineering teams through architecture reviews, implementation decisions, coding practices, design standards, performance tuning, and operational resilience improvements
  • Ensure compliance with engineering, information security, data governance, and regulatory expectations
  • Support Agile delivery, DevSecOps, CI/CD deployment, release readiness, defect resolution, and production support
  • Mentor senior and mid‑level engineers, promote engineering excellence, and drive adoption of enterprise standards
Requirements
  • Bachelor’s degree in information systems, Computer Science, Engineering, Data Engineering, or a related field, or the equivalent combination of education, training, and experience
  • Advanced hands‑on expertise in Spark, Python, SQL, Databricks, Azure Data Factory, Microsoft Fabric, and cloud‑native data integration, transformation, and analytics solutions
  • Strong experience designing, building, and supporting scalable data pipelines, lakehouse architecture, data warehouses, data marts, and analytical data stores
  • Expertise in automated data quality validation, data reconciliation, metadata management, lineage, monitoring, alerting, error handling, and SLA management
  • Experience with governance and catalog platforms such as Unity Catalog, Alation, or similar tools
  • Experience with BI and analytics platforms such as Power BI, Tableau, Microsoft Fabric, and enterprise reporting modernization patterns
  • Working knowledge of Azure DevOps, CI/CD pipelines, Agile delivery, production deployment, and operational support practices
  • Ability to communicate complex technical concepts clearly to business stakeholders, technology leaders, engineers, and cross‑functional delivery teams
  • Strong problem‑solving skills, architectural judgment, ownership mindset, and ability to lead delivery in complex, highly regulated enterprise environments
  • Applicants must be authorized to work in the United States without the need for current or future sponsorship
  • Ability to work Monday–Friday, 8:00AM–4:30PM
Core Competencies

Demonstrates advanced expertise in designing and optimizing scalable data pipelines and cloud‑native analytics solutions using Spark, Python, SQL, and Azure services. Proven ability to lead technical teams, implement data governance practices, and ensure compliance in complex enterprise environments.

Highest‑signal resume keywords
  • Spark
  • Python
  • SQL
  • Azure Data Factory
  • Data Governance
ATS Optimization Keywords Hard Skills
  • Data Engineering
  • Data Pipeline Development
  • Automated Data Quality Validation
  • Metadata Management
  • Data Reconciliation
  • Lakehouse Architecture
  • Data Warehousing
  • Analytical Data Stores
  • ETL Frameworks
  • API‑Driven Data Onboarding
Soft Skills
  • Problem‑Solving
  • Communication
  • Leadership
  • Mentoring
  • Architectural Judgment
Industry Keywords
  • Agile Delivery
  • DevSecOps
  • CI/CD
  • Data Governance
  • Regulatory Compliance
Tools & Technologies
  • Databricks
  • Microsoft Fabric
  • Gravitee
  • MuleSoft
  • Kafka
  • Unity Catalog
  • Alation
  • Power BI
  • Tableau
  • Azure DevOps
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