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

Tata Consultancy Service Limited

Engineer

Addison, TX · On-site

$80K - $90K/yr

Minimum 10+ years Roles & Responsibilities Seeking a Senior Big Data Engineer with 1013 years of ... Collaboration, Leadership & Delivery • Collaborate closely with quants, product owners ...

Amrize

Electrical Engineer I

Midlothian, TX · On-site

... quantitative data, its' organization and analysis using a variety of traditional as well as ... The electrical engineer shall assess the condition of the process and machinery accurately, assess ...

Amrize

Electrical Engineer I

Midlothian, TX · On-site

... quantitative data, its' organization and analysis using a variety of traditional as well as ... Ensure all system programming meets with proper environmental compliance. * Maintain system ...

USAA

Senior Marketing Data Scientist

Plano, TX · On-site

We are looking for a Senior Marketing Data Scientist to join the team. You will lead the technical ... engineering, or other similar quantitative discipline; OR 4 years of experience in statistics ...

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

Quantitative Data Engineer information

See Dallas, TX salary details

$10.9K

$128.3K

$195.9K

How much do quantitative data engineer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for quantitative data engineer in Dallas, TX is $128,270.00, according to ZipRecruiter salary data. Most workers in this role earn between $115,200.00 and $137,000.00 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 Dallas, TX?

For Quantitative Data Engineer jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Quantitative Data Engineer jobs in Dallas, TX look for?

The top searched job categories for Quantitative Data Engineer jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Quantitative Data Engineer jobs?

Cities near Dallas, TX with the most Quantitative Data Engineer job openings:

Quantitative Data Engineer jobs near you

Engineer

Tata Consultancy Service Limited

Addison, TX • On-site

$80K - $90K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 8 days ago

Job description

Must Have Technical/Functional Skills
Primary skills: PySpark, Apache Kafka, Hadoop Ecosystem, Hive, Databricks Lakehouse Architecture, Delta Lake, Bronze/Silver/Gold Data Modeling, Big Data ETL Pipeline Development, SQL, Real-time Data Ingestion Frameworks, Data Governance & Cataloging, CI/CD Tools Git, Jenkins, Bitbucket, Workflow Orchestration, and Cloud & On-Prem Big Data Platforms.
Experience: Minimum 10+ years
Roles & Responsibilities
Seeking a Senior Big Data Engineer with 1013 years of experience specializing in Hadoop, PySpark, Kafka, Hive, and strong experience designing data solutions for large-scale financial systems.
In addition, the candidate must possess advanced expertise in Databricks Lakehouse architecture, particularly around Bronze/Silver/Gold layer data modeling, Delta Lake optimizations, and building reliable, scalable pipelines for regulatory, risk, trading, and analytics workloads.
This role focuses on delivering highly performant, well-governed data platforms that support the banks mission-critical global markets functions.
Key Responsibilities:
Big Data Platform Engineering
• Design, develop, and optimize PySpark-based ETL pipelines running on on-prem Hadoop clusters and cloud environments.
• Build high-volume ingestion frameworks using Kafka for real-time and near-real-time trading and market data.
• Develop, tune, and manage Hadoop ecosystem componentsHDFS, YARN, MapReduce, Tez, Oozie/Airflow.
• Build high-performance, optimized Hive data models for regulatory reporting, trade lifecycle, and market risk processing.
Databricks Lakehouse & Delta Framework
• Architect and implement Bronze/Silver/Gold layer modeling patterns within the Databricks Lakehouse.
• Apply Delta Lake best practices including:
o optimized file management
o Z-Ordering
o Delta Change Data Feed (CDF) o schema evolution & enforcement o ACID transaction handling
• Build reusable frameworks for ingestion, cleansing, transformation, and consumption of data across Lakehouse layers.
• Enable governance, lineage, and auditability using Unity Catalog or equivalent cataloging tools.
Collaboration, Leadership & Delivery
• Collaborate closely with quants, product owners, architects, risk tech, and business users.
• Participate in agile ceremonies sprint planning, refinement, design reviews.
• Mentor junior engineers and contribute to building strong engineering practices across tech teams.
Required Skills & Experience
• 1013 years of hands-on experience in Big Data engineering.
• Expert skills in:
o PySpark dataframe optimizations, partitioning, broadcast strategies, distributed computing.
o Kafka producer/consumer design, schema registry, streaming ETLs.
o Hadoop ecosystem HDFS, YARN, MapReduce/Tez, Oozie/Airflow.
o Hive advanced query tuning, TEZ optimization, partition/bucket management.
• Extensive hands-on experience with Databricks Lakehouse, including:
o Bronze/Silver/Gold layer modeling
o Delta Lake optimizations
o Data quality frameworks on Lakehouse
o Structured & unstructured data handling
• Experience in Global Markets, Risk, Treasury, Trade Surveillance, or Regulatory Reporting.
• Strong SQL knowledge with experience working on massive datasets (TB/PB scale).
Experience with CI/CD practices Git, Jenkins, Bitbucket, build pipelines.
TCS Employee Benefits Summary:
Discretionary Annual Incentive.
Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
Family Support: Maternal & Parental Leaves.
Insurance Options: Auto & Home Insurance, Identity Theft Protection.
Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
Time Off: Vacation, Time Off, Sick Leave & Holidays.
Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.
Salary Range: $80,000- 90,000 a year

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