> Markdown version of [/jobs/ext/1009581-python-data-engineering-professional](https://www.wearedevelopers.com/jobs/ext/1009581-python-data-engineering-professional). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Python data engineering professional - **Company:** Axiom - **Location:** Charlotte, NC, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Server Applications, Unit Testing, Big Data, C Sharp (Programming Language), Cloud Database, Code Review, Databases, Continuous Integration, Data Validation, Information Engineering, Extract Transform Load (ETL), Relational Databases, Database Queries, Software Debugging, Python (Programming Language), Software Engineering, Data Storage Technologies, Azure Data Factory, Backend, AWS Glue, Data Analytics, Api Design, Data Pipelines - **Published:** June 9, 2026 - **Apply:** https://www.dice.com/job-detail/83a89039-0ec7-40a7-ba0b-563d862f51c2 ## About the Role Are you an experienced Python data engineering professional ready to make an impact? * 10+ years of senior-level software engineering, data engineering, or back-end development experience. * Strong hands-on Python development experience, ideally in data-heavy, API-driven, or enterprise back-end environments. * Strong SQL skills with experience working across relational databases and large datasets. * Experience developing ETL pipelines and supporting data quality, transformation, and storage optimization. * Hands-on experience with cloud data tools such as AWS Glue, Azure Data Factory, or comparable platforms. * C# development experience for server-side applications, APIs, or enterprise integrations. * Experience writing unit tests, debugging applications, participating in code reviews, and supporting CI/CD workflows. * Strong analytical thinking, documentation discipline, and ability to work independently or within Agile teams. * Financial services or capital markets experience is a plus. ## Description Join a technology team supporting a leading financial services environment where data-driven systems, scalable applications, and reliable engineering practices are critical to business operations. This team works across technical and business groups to build solutions that collect, process, and deliver large volumes of data for analysis, reporting, and downstream application needs. The environment is collaborative, fast-moving, and suited for a senior engineer who can work independently while partnering closely with cross-functional stakeholders. What's In Store For You: Engagement: W2 only (no C2C/1099) This is a hybrid opportunity based in Charlotte, NC, supporting a long-term technical initiative within a financial services technology group. The role offers exposure to back-end engineering, ETL development, APIs, cloud data tooling, and enterprise-scale data workflows. How You Will Make An Impact * Build and maintain scalable data pipelines that gather, transform, store, and process large volumes of data. * Develop server-side applications, APIs, scripts, and back-end components using Python and C#. * Support ETL development, data quality checks, and optimization of data storage and processing workflows. * Integrate databases, third-party services, cloud data tools, and internal APIs into reliable application solutions. * Write clean, efficient, well-documented code and participate in unit testing, debugging, and code reviews. * Collaborate with business and technical teams to gather requirements, implement solutions, and support CI/CD delivery practices. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Tips, Techniques, and Common Pitfalls Debugging Kafka](https://www.wearedevelopers.com/videos/838-tips-techniques-and-common-pitfalls-debugging-kafka) ## Related Articles - [The 13 Best Python Libraries for Developers in 2025](https://www.wearedevelopers.com/magazine/371-the-13-best-python-libraries-for-developers-in-2025) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)