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Python Data Developer Jobs in Illinois (NOW HIRING)

sgsco

Packaging Data Engineer

Chicago, IL · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Position overview The Packaging Data Engineer will report directly to the Global Head of ... Experience with Python, data pipelines, cloud platforms, data modeling, and Microsoft Copilot a ...

SGS & Co

Packaging Data Engineer

Chicago, IL · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Position overview The Packaging Data Engineer will report directly to the Global Head of ... Experience with Python, data pipelines, cloud platforms, data modeling, and Microsoft Copilot a ...

Schawk USA LLC

Packaging Data Engineer

Chicago, IL · Hybrid

Position overview The Packaging Data Engineer will report directly to the Global Head of ... Experience with Python, data pipelines, cloud platforms, data modeling, and Microsoft Copilot a ...

Long Finch Technologies

Visa Independent Only Must Have Skills Amazon Neptune Graph Database, SPARQL Data Engineering / ETL Pipelines GraphRAG Nice to have skills Gen AI integration, Python Backend API Development Detailed ...

sgsco

Packaging Data Engineer

Chicago, IL · Hybrid

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Position overview The Packaging Data Engineer will report directly to the Global Head of ... Experience with Python, data pipelines, cloud platforms, data modeling, and Microsoft Copilot a ...

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Python Data Developer information

What is a Python data developer?

Python Data Developers are professionals who use the Python programming language to collect, process, and analyze data. They build and maintain data pipelines, write scripts for data manipulation, and work with databases to ensure data is accessible and usable for analytics and business insights. These developers often collaborate with data scientists, analysts, and other IT professionals to support data-driven decision-making within an organization.

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

To excel as a Python Data Developer, you need strong programming skills in Python, a solid understanding of data structures, algorithms, and experience with relational and NoSQL databases. Familiarity with data processing libraries (like Pandas, NumPy), ETL tools, and version control systems, as well as knowledge of cloud platforms (such as AWS or Azure), are typically required. Problem-solving ability, attention to detail, and effective communication are vital soft skills in this role. These skills enable efficient data pipeline development, ensure data quality, and facilitate collaboration within technical teams.

What are some common challenges faced by Python data developers when working with large datasets?

Python Data Developers often encounter challenges related to efficiently processing and managing large datasets, such as optimizing data pipelines for speed and memory usage. Handling data quality issues, integrating data from multiple sources, and ensuring scalability of their solutions are also frequent hurdles. Collaboration with data engineers, analysts, and stakeholders is crucial for understanding requirements and delivering robust results. Staying up to date with the latest libraries and tools, like Pandas, Dask, or PySpark, is also important to overcome these challenges and maintain high performance.

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

AspectPython Data DeveloperData Analyst
Required SkillsPython, SQL, data modeling, ETL processesExcel, SQL, data visualization, basic statistics
CertificationsPython certifications, data engineering coursesData analysis certifications, Excel certifications
Work EnvironmentData engineering teams, software development projectsBusiness units, reporting teams
Industry UsageTech, finance, healthcare, where data pipelines are neededMarketing, finance, operations for insights and reporting

The Python Data Developer focuses on building data pipelines, integrating data sources, and developing scalable data solutions using Python. In contrast, Data Analysts primarily interpret data, create reports, and provide insights for decision-making. While both roles require SQL and data handling skills, Python Data Developers are more involved in data engineering tasks, whereas Data Analysts focus on data visualization and analysis.

What are popular job titles related to Python Data Developer jobs in Illinois?

For Python Data Developer jobs in Illinois, the most frequently searched job titles are:

What job categories do people searching Python Data Developer jobs in Illinois look for?

The top searched job categories for Python Data Developer jobs in Illinois are:

What cities in Illinois are hiring for Python Data Developer jobs?

Cities in Illinois with the most Python Data Developer job openings:

Python Data Developer jobs near you

Senior Graph Database Developer

Long Finch Technologies

Lyons, IL • Hybrid

Contractor

Posted 4 days ago

Job description

Job title: Senior Graph Database Developer

Work Location: Chicago, IL, 60606(Hybrid )

Duration: 6+ Months Contract
Experience Level: 10+ Years

Visa: Visa Independent Only

Job Description:
Must Have Skills
Amazon Neptune Graph Database, SPARQL
Data Engineering / ETL Pipelines
GraphRAG
Nice to have skills
Gen AI integration, Python  Backend API Development
Detailed Job Description
Education Bachelors or Masters degree in Computer Science, Data Science, Information Architecture, or a related field.
Graph Databases Extensive hands on experience designing and managing production scale instances of Amazon Neptune.
Graph languages, specifically Gremlin or SPARQL.
Programming Advanced production coding in Python or JVM environments Java, Scala.
Cloud Infrastructure Strong proficiency with AWS Services e.g., AWS S3, Amazon RDS, Amazon ECS
Top 3 responsibilities you would expect the Subcon to shoulder and execute
Design enterprise ontologies, taxonomies, and graph schemas that reflect complex business structures.
Architect, configure, and scale Amazon Neptune database environments to handle billions of relationships.
Integrate knowledge graphs into GraphRAG pipelines to enhance contextual grounding and minimize AI hallucinations. Partner with AI teams to implement hybrid search architectures combining graph traversals with vector databases.

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