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Python Data Cleaning Jobs (NOW HIRING)

VC5 Consulting

Python Data Engineer

Houston, TX · On-site

... clean, well-architected code. * Background in financial trading, commodities, oil and gas, or a ... Location: Houston, Texas 77098 If you are a Python Data Engineer with deep financial or energy ...

Mindrift

Freelance Data Scraping Engineer (Python)

$37/hr

Mindrift is looking for highly skilled Python Data Scraping Engineers to join the Tendem project ... Solid background in data cleaning, normalization, and validation, delivering structured datasets ...

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Python Data Cleaning information

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How much do python data cleaning jobs pay per hour?

As of Jun 13, 2026, the average hourly pay for python data cleaning in the United States is $58.62, according to ZipRecruiter salary data. Most workers in this role earn between $48.32 and $66.59 per hour, depending on experience, location, and employer.

What is the difference between Python Data Cleaning vs Data Analyst?

AspectPython Data CleaningData Analyst
Required SkillsPython, data cleaning libraries (pandas, NumPy)Excel, SQL, data visualization
Work EnvironmentData processing, scripting, automationData interpretation, reporting, presentations
Industry UsageData preparation for analysis or modelingBusiness insights, decision-making
CertificationsPython certifications, data analysis coursesBusiness analysis, Excel certifications

Python Data Cleaning focuses on preparing raw data using programming skills, primarily for further analysis or modeling. Data Analysts interpret cleaned data to generate insights and reports. While both roles require understanding data, Python Data Cleaning emphasizes scripting and automation, whereas Data Analysts focus on analysis and communication.

What are some common challenges faced in a Python data cleaning role, and how can they be addressed?

Professionals in Python data cleaning roles often encounter challenges such as dealing with inconsistent data formats, missing or duplicate values, and integrating data from multiple sources. These issues require strong attention to detail and proficiency with Python libraries like pandas and NumPy. Effective communication with data engineers and domain experts is also important to clarify data definitions and ensure accuracy. Regularly documenting cleaning processes and using version control helps maintain data quality and reproducibility, making teamwork more efficient.

What are the key skills and qualifications needed to thrive as a Python Data Cleaning specialist, and why are they important?

To excel in Python Data Cleaning, you need strong analytical skills, proficiency with Python programming, and a solid understanding of data manipulation concepts, often supported by a degree in computer science, statistics, or a related field. Familiarity with libraries such as pandas, NumPy, and data visualization tools, along with knowledge of SQL databases, is typically required. Attention to detail, problem-solving abilities, and effective communication help you identify and resolve data quality issues efficiently. These skills are essential to ensure accurate, reliable datasets that drive meaningful analysis and business decisions.

What is Python data cleaning?

Python data cleaning is the process of using Python programming language and its libraries to identify, correct, or remove inaccurate, incomplete, or irrelevant data from datasets. This step is crucial in data analysis and machine learning, as clean data leads to more reliable and meaningful results. Tasks involved may include handling missing values, correcting data types, removing duplicates, and standardizing formats. Popular Python libraries for data cleaning include pandas and NumPy. Effective data cleaning ensures that analyses and models are built on high-quality, trustworthy data.
Infographic showing various Python Data Cleaning job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 86% Full Time, and 13% Part Time. Highlights an 100% Physical job distribution, with an average salary of $121,932 per year, or $58.6 per hour.
Python Data Engineer

Python Data Engineer

VC5 Consulting

Houston, TX • On-site

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

A well-established energy trading firm in Houston is seeking a seasoned Python Data Engineer to expand its data assets and directly elevate its analytical and trading capabilities. In this role, you will sit at the intersection of engineering and the business — working shoulder-to-shoulder with traders, researchers, analysts, and data scientists to define requirements and deliver production-grade data solutions. You will own the design and implementation of modular, reusable data ingestion pipelines, ensuring code quality and architectural consistency across a centralized codebase. This is a high-impact individual contributor role for an engineer who thrives in a fast-paced, collaborative environment and wants their work to have a measurable effect on real trading decisions.

Key Responsibilities:

  • Interface directly with traders, analysts, researchers, and data scientists to gather, refine, and prioritize data requirements, translating complex business needs into functional, production-ready Python code.

  • Design, build, and maintain robust ETL and data ingestion pipelines using Python and Pandas, ensuring data accuracy, reliability, and performance at enterprise scale.

  • Write modular, reusable components for data interaction that conform to existing architecture patterns, coding conventions, and centralized codebase standards.

  • Coordinate efficiently with a globally distributed team of developers and business sponsors to deliver data solutions that span time zones and organizational boundaries.

  • Participate actively in the internal Python development community, acting as a technical liaison and working to standardize and consolidate core functionality within shared developer tooling.

  • Perform high-level technical troubleshooting across the data platform, diagnosing issues within a modular component architecture and driving them to resolution with minimal disruption to downstream consumers.

Requirements:

  • 6 to 15 years of professional, enterprise-level Python development experience — candidates must be able to demonstrate a strong tenure record at one or a small number of employers.

  • Deep, hands-on proficiency with the Pandas library for data manipulation, transformation, and pipeline development in a production environment.

  • Proven experience building and maintaining ETL and data ingestion pipelines at enterprise scale, with a track record of delivering clean, well-architected code.

  • Background in financial trading, commodities, oil and gas, or a large banking/financial services firm — this domain context is essential for working effectively alongside trading and research teams.

  • Demonstrated ability to gather technical requirements directly from non-engineering stakeholders such as traders, analysts, and researchers, and translate those needs into reliable technical solutions.

  • Strong collaborative instincts and the communication skills to work across global development teams, contributing to shared codebases and upholding engineering standards.

Role Details:

  • Employment Type: Direct Hire

  • Location: Houston, Texas 77098

If you are a Python Data Engineer with deep financial or energy trading domain experience and a passion for building data infrastructure that drives real business decisions, we encourage you to apply.


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