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Python Data Jobs in New Jersey (NOW HIRING)

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Data Engineer (AWS & Python)

Newark, NJ · On-site

$119K - $143K/yr

The ideal candidate will have extensive hands-on experience building scalable data pipelines using Python and AWS cloud services. An active AWS Certification and exceptional communication skills (10 ...

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Python Data information

What is a Python Data professional?

A Python Data professional is someone who uses the Python programming language to analyze, process, and interpret data. They work with large datasets, perform data cleaning and transformation, and apply statistical or machine learning techniques to extract insights. These professionals often work in roles such as data analyst, data scientist, or data engineer, and use Python libraries like Pandas, NumPy, and scikit-learn to accomplish their tasks.

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

To thrive as a Python Data professional, you need strong programming skills in Python, a solid understanding of data structures, algorithms, and experience with data analysis or data science, typically supported by a relevant degree. Familiarity with technical tools such as pandas, NumPy, SQL, Jupyter Notebooks, and often cloud platforms or machine learning frameworks is important, and certifications like Microsoft or Google Data certifications can be advantageous. Strong analytical thinking, attention to detail, and effective communication help you extract insights from data and collaborate with stakeholders. These skills and qualities are essential to efficiently process, analyze, and interpret data, driving informed business decisions.

What are some common challenges faced by Python Data professionals when working with large datasets?

Python Data professionals often encounter challenges such as optimizing code to handle large volumes of data efficiently and managing memory usage to prevent slowdowns or crashes. Working with big datasets may require leveraging tools like pandas, NumPy, or Dask, and sometimes integrating with distributed computing systems such as Apache Spark. Additionally, ensuring data quality and managing data pipelines for consistent and accurate results can be demanding. Collaborating closely with data engineers, analysts, and other stakeholders is common to ensure smooth data flow and analysis.

What is the difference between Python Data vs Data Analyst?

AspectPython DataData Analyst
Required SkillsPython programming, data manipulation, scriptingExcel, SQL, data visualization
CertificationsPython certifications, data science coursesData analysis certifications, Excel certifications
Work EnvironmentData science teams, programming-heavy rolesBusiness intelligence, reporting teams
Industry UsageTech, finance, healthcareRetail, marketing, finance

Python Data roles focus on programming, data manipulation, and building data pipelines using Python, while Data Analysts primarily analyze data using tools like Excel and SQL to generate reports and insights. Both roles often collaborate but differ in technical depth and tools used.

Are Python coders in demand?

Python developers are in high demand across various industries due to the language's versatility in data analysis, web development, and automation. Employers seek skills in frameworks like Django and data tools such as Pandas, making Python a valuable programming language for job seekers. The demand is expected to grow as data-driven decision-making and automation increase in the workplace.

How much do Python data scientists make?

Python data scientists typically earn a median salary ranging from $90,000 to $130,000 annually, depending on experience, location, and industry. Advanced skills in machine learning, data analysis, and proficiency with tools like Pandas and TensorFlow can lead to higher compensation.

Is Python Data still in demand in 2026?

Python data roles remain in high demand in 2026 due to the language's widespread use in data analysis, machine learning, and automation. Skills in libraries like Pandas, NumPy, and frameworks such as TensorFlow enhance job prospects, and proficiency in data management tools is often required.

What are popular job titles related to Python Data jobs in New Jersey?

For Python Data jobs in New Jersey, the most frequently searched job titles are:

What job categories do people searching Python Data jobs in New Jersey look for?

The top searched job categories for Python Data jobs in New Jersey are:

Python Data jobs near you
Infographic showing various Python Data job openings in New Jersey as of August 2026, with employment types broken down into 58% Full Time, 21% Part Time, and 21% Contract. Highlights an 100% In-person job distribution.

Lead Software Engineer - Python/ Data

JPMorgan Chase & Co.

Jersey City, NJ • On-site

$120 - $150/hr

Other

Re-posted 18 days ago

JPMorgan Chase & Co. rating

8.0

Company rating: 8.0 out of 10

Based on 495 frontline employees who took The Breakroom Quiz

72nd of 171 rated banks

Job description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within Corporate Technology in Financial Planning & Analysis team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market‑leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job Responsibilities

  • Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems.

  • Develops secure and high‑quality production code, and reviews and debugs code written by others.

  • Drives team adoption of enterprise‑authorized AI‑assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI‑assisted code review/refactoring, test strategy acceleration, incident/root‑cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
  • Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise‑authorized AI‑assisted development and automation capabilities, to improve the value realized by automation.

  • Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.

  • Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes‑oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture.

Required Qualifications, Capabilities, and Skills

  • A. Formal training or certification on software engineering concepts and 5+ years applied experience (NAMR/APAC – India/LATAM/Hong Kong)
    B. Formal training or certification on software engineering concepts and advanced applied experience (EMEA/LATAM‑Brazil)
    C. Singapore follow local country guidance

  • Hands‑on practical experience delivering system design, application development, testing, and operational stability.

  • Advanced in one or more programming languages including Java, Python, or Scala.

  • Good understanding of the AWS platform, including its core services, architecture best practices, and security features.
  • Extensive experience with big data technologies, including Apache Spark, for large‑scale data processing and analytics.
  • Demonstrated experience leading effective use of approved AI‑assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
  • Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.

  • Proficient in all aspects of the Software Development Life Cycle.

  • Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.

  • In-depth knowledge of the financial services industry and their IT systems
    Practical cloud native experience.

Preferred Qualifications, Capabilities, and Skills

  • Exposure to Databricks.

  • Experience with data visualization and business intelligence platforms.

  • Bachelor's degree in Finance, Technology, Data Science, or a related field; advanced degree preferred.

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What JPMorgan Chase & Co. employees say

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