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Quantitative Data Engineer Jobs in Missouri (NOW HIRING)

TekWissen LLC

IT Data Engineer (Entry)

Creve Coeur, MO · On-site

$58/hr

Job Title: IT Data Engineer (Entry) Location: Creve Coeur, MO 63167 Duration: 12 Months Job Type ... Bachelor's degree in data science, computer science, or related quantitative field * A minimum of 3 ...

Amrize

Electrical Engineer, New Graduate

Bloomsdale, MO · 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 ...

Equifax, Inc.

Data Analyst

Saint Louis, MO · On-site

Advanced degree in a quantitative field * Experience in a professional services consulting role ... Experience with product management, product ownership, or data engineering * Experience in Human ...

Embtel, Inc.

Jr. Data Scientist

Saint Louis, MO · On-site

... Engineering, Experimental Science with 3+ years of experience or Bachelor's degree and 5+ years of quantitative analysis experience in data science capabilities including data mining, predictive ...

Embtel, Inc.

Jr. Data Scientist

Saint Louis, MO

... Engineering, Experimental Science with 3+ years of experience or Bachelor's degree and 5+ years of quantitative analysis experience in data science capabilities including data mining, predictive ...

ConfigUSA

Data Scientist

Saint Louis, MO · On-site

Engineering, Social Science, or Statistics) • Must have 2+ years of experience with predictive ... and/or other forms of quantitative analysis • Must have experience using libraries like ...

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All JobsQuantitative Data Engineer JobsQuantitative Data Engineer Jobs in Missouri

Quantitative Data Engineer information

What is an example of quantitative?

A quantitative example involves numerical data that can be measured and analyzed statistically. In a Quantitative Data Engineer role, this might include metrics like transaction volumes, data throughput, or statistical summaries used to build data pipelines and models.

What is quantitative vs qualitative?

Quantitative data refers to numerical information that can be measured and analyzed statistically, such as sales figures or sensor readings. Qualitative data involves descriptive, non-numerical information like opinions, interviews, or observations. In data engineering, understanding both types helps in designing systems that process and analyze diverse data sources effectively.

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 does quantitative mean?

In the context of a Quantitative Data Engineer role, 'quantitative' refers to working with numerical data and statistical methods to analyze and model information. This involves skills in mathematics, programming, and data analysis tools to develop algorithms and insights from large datasets.

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 are the synonyms of quantitative?

Synonyms of quantitative include numerical, measurable, and statistical. In a data engineering context, these terms relate to data that can be quantified and analyzed using tools like SQL, Python, or R, often involving numerical datasets and statistical methods.

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

Data Engineer, Data Solutions

NISA Investment Advisors

Clayton, MO • On-site

$107K - $128K/yr

Other

Posted 7 days ago

Job description


Data Engineer, Data Solutions
Overview

NISA Investment Advisors, LLC (NISA) partners with world-leading organizations to design, develop, and manage highly customized, risk-controlled investment strategies across fixed income, equities, and derivatives. With $462 billion assets under management ($295 billion in physical assets and $167 billion in derivatives notional value), NISA actively manages risk for institutional investors, providing clarity to complicated challenges and stability in ever-evolving markets. At NISA, we foster a culture that supports both personal and professional growth, providing opportunities to learn from experienced professionals while contributing meaningful work from the outset. We seek candidates who demonstrate strong quantitative and analytical skills, intellectual curiosity, and a collaborative mindset to join our growing teams.

Responsibilities

The Data Engineer, Data Solutions is a member of NISA's Data Engineering team responsible for creating, updating and supporting ETL/ELT data pipelines for a variety of stakeholders across the enterprise. They will define, design, develop, unit test and support new and existing solutions to meet complicated and time-sensitive business requirements. In addition, the Data Engineer supports and manages high-severity production incident resolution, performs code reviews, and participates in on-call rotations. They should be comfortable working in a fast-paced team environment and have the skills to troubleshoot and resolve data integration issues independently.

Qualifications
    Bachelor's degree in Computer Science or a related field or relevant work experience required
  • 5+ years of experience with ETL/ELT
  • 3+ years of experience with SQL
  • Experience with Python, C#, or similar high-level language
  • Experience with AWS, AWS Lambda, S3, Airflow, and related technologies
  • Professional work experience with Databricks or PySpark
  • Demonstrated team player who works well in a fast-paced environment and can adapt to changing priorities, demands, and timelines
  • Strong analytical and innovative problem-solving skills, along with a solid attention to detail.
  • Excellent customer support skills

Preferred Experience:

  • Microsoft SQL Server database development
  • PostgreSQL database development
  • SSIS development
  • Working knowledge of Airflow or a similar business automation tool
  • Developing software/databases in an Agile environment

NISA's culture encourages collaboration and innovation. We seek self-motivated, intellectually curious individuals willing to push themselves and others in an environment that celebrates fresh thinking. We equip employees with the resources needed to excel and we encourage personal development. NISA is dedicated to internally cultivating and rewarding talent. Employees at NISA are provided with a wide range of benefits, including a complimentary onsite cafeteria, gym, health and wellness program, and educational assistance program.

NISA is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.

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