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Quantitative Data Engineer Jobs in Washington, DC

VTG

Data Analyst with Security Clearance

Chantilly, VA · On-site

Apply qualitative and quantitative data analysis methodologies to complex business and mission ... Transform, manipulate, combine, and analyze data using programming languages such as Python or R.

New

Elder Research

Data Scientist

Arlington, VA · On-site

$80 - $100/hr

Experience in quantitative statistical approaches to anomaly detection to identify non-compliance ... Prior computer programming experience, preferably in a language such as Python or R * Experience ...

Kanak Elite Services Inc

Senior Data Engineer

Arlington, VA · On-site

$121K - $165K/yr

Senior Data Engineer Location : Arlington, VA Hybrid (Need local candidates only within 30-40 miles ... Passion for analytical / quantitative problem solving * Experience identifying and implementing ...

Ventus Solutions

Quantitative Analyst SETA

Arlington, VA · On-site

$150 - $195/hr

... network data science/data engineering applied to complex adaptive systems. * Demonstrated ... Experience in a high-paced private-sector quantitative production environment, such as systematic ...

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All JobsQuantitative Data Engineer JobsQuantitative Data Engineer Jobs in Washington, DC

Quantitative Data Engineer information

See Washington, DC salary details

$12.5K

$146.9K

$224.3K

How much do quantitative data engineer jobs pay per year?

As of Sep 4, 2026, the average yearly pay for quantitative data engineer in Washington, DC is $146,860.00, according to ZipRecruiter salary data. Most workers in this role earn between $131,900.00 and $156,900.00 per year, depending on experience, location, and employer.

What is a quantitative data engineer?

A Quantitative Data Engineer is a professional who designs, builds, and maintains data infrastructure that supports quantitative analysis, typically in finance or technology sectors. They work closely with quantitative analysts and data scientists to ensure efficient data pipelines, data quality, and high-performance systems for processing large datasets. Their responsibilities include developing ETL processes, optimizing databases, and implementing data models to support research and trading strategies. Strong programming skills, expertise in big data technologies, and knowledge of quantitative methods are essential for this role.

How does a quantitative data engineer typically collaborate with data scientists and quantitative analysts on projects?

Quantitative Data Engineers work closely with data scientists and quantitative analysts to design, build, and optimize data pipelines that support complex modeling and analytics. They are often responsible for ensuring data quality, scalability, and efficient data processing, enabling analysts to focus on developing models and extracting insights. Regular collaboration includes translating analytical requirements into technical solutions, troubleshooting data issues, and iterating on data infrastructure to support evolving project needs. This teamwork fosters an environment where technical and analytical expertise complement each other, leading to more robust and actionable results.

What are the key skills and qualifications needed to thrive as a quantitative data engineer, and why are they important?

To excel as a Quantitative Data Engineer, you need strong proficiency in programming (such as Python, R, or C++), advanced mathematical and statistical knowledge, and a relevant degree in computer science, mathematics, or a related field. Experience with big data tools (like Spark, Hadoop), cloud platforms, and data pipeline systems, as well as familiarity with financial data sets, is typically required. Analytical thinking, detail orientation, and effective problem-solving skills distinguish top performers in this role. These competencies are critical for efficiently transforming complex data into actionable insights and supporting robust quantitative models in data-driven environments.

What is the difference between Quantitative Data Engineer vs Data Scientist?

AspectQuantitative Data EngineerData Scientist
Primary FocusBuilding data pipelines, data infrastructure, and ensuring data qualityAnalyzing data, creating models, and deriving insights
Skills & ToolsSQL, Python, Spark, ETL processes, data architectureStatistics, machine learning, Python/R, data visualization
CredentialsComputer science, engineering, or related degrees; certifications in data engineeringStatistics, data science, or related degrees; certifications in data analysis or machine learning
Work EnvironmentData engineering teams, data infrastructure projectsData analysis teams, research, and modeling projects

While both roles work closely with data, Quantitative Data Engineers focus on building and maintaining data systems, whereas Data Scientists analyze data to generate insights and models. They often collaborate but have distinct skill sets and responsibilities within data-driven organizations.

What are popular job titles related to Quantitative Data Engineer jobs in Washington, DC?

For Quantitative Data Engineer jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Quantitative Data Engineer jobs in Washington, DC look for?

The top searched job categories for Quantitative Data Engineer jobs in Washington, DC are:

Quantitative Data Engineer jobs near you

Quantitative Analyst SETA

Blue Sky Innovators

Arlington, VA • On-site

$195K - $300K/yr

Full-time

Medical, Dental, Vision, Retirement

Posted 10 days ago

Job description

Blue Sky Innovators is seeking a Quantitative Analyst SETA to join our team in Arlington, VA. The successful candidate will support DARPA program managers in the development, integration, and scaling of complex adaptive system modelling and data-intensive analytical capabilities. This role involves integrating complex, multi-source intelligence and commercial/open-source data streams into graph-based, network analytical frameworks to support strategic competition analysis, industrial base resilience, and national security decision-making.

Requirements:

  • Master's degree or PhD in Economics, Data Science, Applied Mathematics, or a related quantitative field.
  • 5+ years of relevant experience in quantitative economic modeling, advanced econometrics, and network data science/data engineering applied to complex adaptive systems.
  • Demonstrated experience in large-scale data processing, multi-source data pipeline integration, and managing structured/unstructured data architectures.
  • Practical experience with graph analytics, network modeling tools, and interconnected data architectures (e.g., Python/R quantitative libraries, graph databases, or network science frameworks).
  • Strong background working within or alongside the U.S. Intelligence Community (IC), including familiarity with IC mission environments, data workflows, and intelligence-derived datasets.
  • Understanding of diverse data sources across global economics, financial networks, trade flows, defense industrial supply chains, and multi-INT sources.
  • Strong communication skills-both written (including executive PowerPoint briefs) and oral-with the ability to translate complex econometric and data models for senior defense stakeholders.
  • Top Secret clearance with SCI eligibility.

Preferred:

  • Active Special Access Program (SAP) access and experience working at TS/SCI and SAP levels.
  • Experience with defense industrial base analysis, economic statecraft, strategic competition modeling, or macroeconomic resilience metrics.
  • Hands-on experience building decision-support tools, AI/ML-enabled analytical tools, cloud data engineering workflows, or large language model (LLM) research pipelines.
  • Prior background supporting new program start-ups, pilot demonstrations, and transition pathways within defense or intelligence organizations.
  • Experience in high paced private sector quantitative production environments such as systematic investing or trading, real time ad technology development or pharmacological optimization and customization.
  • Demonstrated ability to bridge communication and technical execution across academic economists, data engineers, software developers, and operational intelligence analysts.

Responsibilities:

  • Oversee the design, evaluation, and application of quantitative economic models, network analysis frameworks, and graph-based data integration architectures.
  • Oversee the integration of complex, multi-source intelligence and economic datasets into scalable, operational analytical pipelines.
  • Serve as primary technical liaison between academic researchers, software engineering teams, and IC stakeholders to ensure tools align with operational requirements.
  • Advise DARPA leadership on program execution risks, data architecture scalability, and capability transition strategy.
  • Prepare technical documentation, program roadmaps, and executive briefs to communicate program progress and analytical findings to senior decision-makers.

The salary range for this position is $195,000-$300,000, per year. Specific compensation will be determined by several factors including experience, education, skills, and knowledge. We also offer medical/dental/vision benefits and 401k contribution.

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