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Quantitative Data Engineer Jobs in Austin, TX (NOW HIRING)

Apple

Finance Data Engineer

Austin, TX · On-site

$113K - $136K/yr

The Finance Data Engineer is a technical expert who creates data interfaces, pipelines and codebase ... other quantitative discipline required with five or more years of experience Preferred ...

Apple

Finance Data Engineer

Austin, TX · On-site

$113K - $136K/yr

The Finance Data Engineer is a technical expert who creates data interfaces, pipelines and codebase ... other quantitative discipline required with five or more years of experience Preferred ...

Apple

Finance Data Engineer

Austin, TX

$149K - $249K/yr

The Finance Data Engineer is a technical expert who creates data interfaces, pipelines and codebase ... quantitative discipline required with five or more years of experience Pay & Benefits At Apple ...

Apple

Finance Data Engineer

Austin, TX

$149K - $249K/yr

The Finance Data Engineer is a technical expert who creates data interfaces, pipelines and codebase ... quantitative discipline required with five or more years of experience Pay & Benefits At Apple ...

Apple

Finance Data Engineer

Austin, TX

$149K - $249K/yr

The Finance Data Engineer is a technical expert who creates data interfaces, pipelines and codebase ... quantitative discipline required with five or more years of experience Pay & Benefits At Apple ...

Apple

Finance Data Engineer

Austin, TX

$149K - $249K/yr

The Finance Data Engineer is a technical expert who creates data interfaces, pipelines and codebase ... quantitative discipline required with five or more years of experience Pay & Benefits At Apple ...

Eka Finance

Quantitative Research Analyst

Austin, TX · On-site

You will work closely with portfolio managers, developers, and other researchers to turn data into ... Strong quantitative and analytical skills with experience working on large and complex datasets

Deloitte

Lead Generative AI Data Engineer III

Austin, TX · On-site

$101K - $133K/yr

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field. * 7+ years of professional experience. * 5+ years of experience designing ...

Deloitte

Lead Generative AI Data Engineer III

Austin, TX · On-site

$101K - $133K/yr

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field. * 7+ years of professional experience. * 5+ years of experience designing ...

Virtu Financial

Quantitative Strategist (PhD)

Austin, TX · On-site

$175K - $200K/yr

Create tools to automate research tasks and improve visualization of complex data sets THE CANDIDATE * PhD in Science, Math, Engineering or other quantitative or STEM programs. * No previous Quant ...

Cushman & Wakefield

Junior Data Scientist

Austin, TX · On-site

... quantitative urban planner, and who brings the technical skills to build and operate the data infrastructure their own work requires. This is not primarily an engineering role, though the ideal ...

Cushman & Wakefield

Junior Data Scientist

Austin, TX · On-site

... quantitative urban planner, and who brings the technical skills to build and operate the data infrastructure their own work requires. This is not primarily an engineering role, though the ideal ...

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Showing results 1-20

All JobsQuantitative Data Engineer JobsQuantitative Data Engineer Jobs in Austin, TX

Quantitative Data Engineer information

See Austin, TX salary details

$10.9K

$128.5K

$196.3K

How much do quantitative data engineer jobs pay per year?

As of Aug 10, 2026, the average yearly pay for quantitative data engineer in Austin, TX is $128,526.00, according to ZipRecruiter salary data. Most workers in this role earn between $115,500.00 and $137,300.00 per year, depending on experience, location, and employer.

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 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.

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.

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 popular job titles related to Quantitative Data Engineer jobs in Austin, TX? For Quantitative Data Engineer jobs in Austin, TX, the most frequently searched job titles are:
What job categories do people searching Quantitative Data Engineer jobs in Austin, TX look for? The top searched job categories for Quantitative Data Engineer jobs in Austin, TX are:
What cities near Austin, TX are hiring for Quantitative Data Engineer jobs? Cities near Austin, TX with the most Quantitative Data Engineer job openings:
Quantitative Data Engineer jobs near you

Data Engineer

Texas State Library and Archives Commision

Austin, TX • On-site

$113K - $136K/yr

Full-time

Re-posted 22 days ago

Job description

We're looking for a Data Engineer to help build and maintain the data pipelines that power our investment, research, and analytics teams. You'll work closely with data scientists, quants, and investors to onboard new datasets, ensure data quality, and maintain the reliability of the data that drives decision-making across the firm.
What You'll Do
• Build, maintain, and troubleshoot ETL pipelines (Airflow, Dagster, or similar).
• Ingest and deeply understand new datasets - their structure, quirks, and business meaning.
• Maintain high-quality, well-documented datasets used across the organization.
• Partner with non-engineering stakeholders to understand data needs and guide them to the right sources.
• Evaluate data vendors and ensure we use the best data for each use case.
What We're Looking For
• Strong Python and SQL skills.
• Experience building data pipelines; familiarity with Spark or Pandas a plus.
• Strong attention to detail and persistence in debugging data issues.
• Clear communication skills, especially with non-technical audiences.
• 1-3 years of experience (or strong internships); senior candidates also welcome.
Who Thrives Here
• Curious, detail-oriented engineers who like diving deep into complex datasets.
• People who enjoy owning problems end-to-end and defining their own requirements.
• Engineers who build reliable, maintainable systems and prefer fast, iterative execution.
Why Join
• High-impact role: the data you manage powers investment decisions across the firm.
• Broad exposure to many types of financial and alternative data.
• Opportunity to shape a growing data function and work with teams across the entire company.
Qualifications
• Strong Python and SQL skills.
• Experience building data pipelines; familiarity with Spark or Pandas a plus.
• Strong attention to detail and persistence in debugging data issues.
• Clear communication skills, especially with non-technical audiences.
• 1-3 years of experience (or strong internships); senior candidates also welcome.
Why is This a Great Opportunity
You are building data infrastructure that directly drives investment decisions. The pipelines you own power research, analytics, and live decision making across the firm. This is not abstract data work. It affects capital allocation.
You work directly with quants, data scientists, and investors. You are not buried behind layers of product or management. You see how data is used, where it breaks, and how to make it better. That feedback loop is fast and real.
You get broad exposure to high value datasets. Market data, alternative data, vendor feeds, internal research outputs. You learn how data actually behaves in production, not how it looks in a demo.

Apply