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Weekend Data Science Internships Jobs in Texas (NOW HIRING)

SpaceX

Data Engineer (Starlink Growth)

Bastrop, TX · On-site

$113K - $136K/yr

Bachelor's degree in computer science, physics, mathematics, statistics, or a STEM discipline * 1+ ... Must be willing to work extended hours and weekends as needed

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Weekend Data Science Internships information

What is the difference between Weekend Data Science Internships vs Weekend Data Analyst Internships?

AspectWeekend Data Science InternshipsWeekend Data Analyst Internships
Required SkillsBasic programming, statistics, data manipulationData interpretation, reporting, Excel skills
Work EnvironmentCollaborative, project-based, research-focusedBusiness-oriented, reporting, dashboards
Industry UsageTech, finance, healthcare, academiaRetail, marketing, finance, consulting

Weekend Data Science Internships focus on developing skills in programming, machine learning, and statistical analysis, often involving research projects. Weekend Data Analyst Internships emphasize data interpretation, reporting, and visualization for business insights. Both roles are valuable for gaining industry experience but differ in technical depth and application focus.

What are the most commonly searched types of Data Science Internships jobs in Texas? The most popular types of Data Science Internships jobs in Texas are:
What cities in Texas are hiring for Weekend Data Science Internships jobs? Cities in Texas with the most Weekend Data Science Internships job openings:
Weekend Data Science Internships jobs near you

Data Engineer

Texas State Library and Archives Commision

Austin, TX • On-site

$113K - $136K/yr

Full-time

Posted 27 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