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Python Data Developer Jobs in Pennsylvania (NOW HIRING)

Dlapiper

Data Engineer

Philadelphia, PA

$115K - $138K/yr

Together. Summary The Data Engineer, Solutions & Data role designs, builds, and operates data ... Proficiency in SQL and Python. * Data pipeline tooling and cloud data services experience (Azure ...

Sarian, Inc.

Python Developer

Pittsburgh, PA · On-site

$48.75 - $67.25/hr

Python Developer Location: Pittsburgh Fulltime position Onsite position JD: * 8-12 years of ... Data Analysis/Machine Learning. * Testing/Debugging/Security. * Writing, testing, and debugging ...

Marencor

Python/AI/ML Engineer

Malvern, PA · On-site

... developer with a year or two experience with generative AI (genAI is only a few years old from the ... Collaborate with data scientists, product managers, and other stakeholders to understand ...

Envision Technology Solutions

Python Developer

Malvern, PA · On-site

$49.25 - $68/hr

... data streaming technologies (e.g., Kafka, Kinesis) Exposure to Agile/Scrum methodologies 4+ years of experience in design and implementation of large enterprise public cloud environments - AWS ...

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Python Data Developer information

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 Python Data Developers?

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 popular job titles related to Python Data Developer jobs in Pennsylvania? For Python Data Developer jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Python Data Developer jobs in Pennsylvania look for? The top searched job categories for Python Data Developer jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Python Data Developer jobs? Cities in Pennsylvania with the most Python Data Developer job openings:
Python Data Developer jobs near you
Infographic showing various Python Data Developer job openings in Pennsylvania as of July 2026, with employment types broken down into 1% As Needed, 79% Full Time, 12% Part Time, and 8% Contract. Highlights an 81% Physical, 3% Hybrid, and 16% Remote job distribution.

Python Data Engineering with Agentic AI or Declarative Agentic Framework (DAF)

SANS

Philadelphia, PA • On-site

$75/hr

Other

Posted 23 days ago

Job description

Note: Only candidates with 10+ years working in USA companies will be considered.
Please do not send your resume if you do not have 10 or more years of working in USA for name brand companies and at least 2 experience contracts at investment banks or investment managers.


$75/hour corp to corp for Python Data Integration Consultant with AI Declarative Agentic Framework (DAF)

Kindly send this job description to any of your friends who can work this job located in Philadelphia, PA for 1 day a week in the office and 4 days work from home, Job Title - Python Data Integration Consultant
Location - Philadelphia, PA (hybrid)
Duration - 6+- 12+ Months
Hybrid - one time per week in the office and 4 days from home Pay: $600/professional day only C2C or $75/hour corp to corp
The Engagement:
Client is placing a senior data integration consultant embedded at a financial services client. The consultant works onsite as part of the client's data engineering effort, integrating the client's systems and data sources with Client's Declarative Agentic Framework (DAF) and connected products.
This is delivery work, not advisory. The consultant owns the data pipelines, warehouse structures, and integration points that the deployment depends on, and is accountable for data quality and reliability in production.
What the Consultant Will Do

  • Design and build data integration pipelines connecting client source systems (reference data, pricing, transaction, custody, or similar) to client's AI's platform.
  • Own data warehouse and data lake architecture for the engagement, including ingestion, transformation, and data quality rules.
  • Work hands-on with ETL tooling (Informatica, Snowflake, or equivalent) and cloud data platforms (AWS or Azure).
  • Write production-grade Python for data processing and integration between on-prem and cloud systems.
  • Define data reconciliation and data lineage processes so the client can trust what the pipelines produce.
  • Coordinate with the client's data engineering, ops, and compliance stakeholders, and with client's AI's deployment team.
  • Document architecture decisions and integration patterns so the work is defensible and repeatable across future deployments.

Required Background

  • 10+ years in enterprise data architecture, data engineering, or solution architecture roles.
  • Direct experience in financial services, ideally capital markets, asset management, or securities operations. Candidate should recognize terms like reference data, corporate actions, and reconciliation without explanation.
  • Hands-on ETL and data warehousing expertise: Informatica, Snowflake, or comparable enterprise-grade tooling.
  • Production Python for data pipelines and integration work, not scripting on the side.
  • Experience with REST APIs, message queues (Kafka or similar), and containerized deployments (Docker).
  • Track record owning delivery end to end, from data analysis and architecture through production support, ideally with distributed or offshore team coordination.
  • Comfortable being the senior technical presence in the room with client stakeholders from day one. No ramp-up period.

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