Senior Azure Data Engineer Capital Markets Data Platform
Axiom Path Charlotte, North Carolina, United States
IT Services and IT Consulting · 51-200 employees
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About the role
Design, develop, and enhance a strategic enterprise data platform to support capital markets and securities operations. Build scalable data pipelines and processing solutions while collaborating with distributed teams to deliver production-ready data services.
What they look for
Requirements
Requires 10 or more years of software development or data engineering experience with advanced proficiency in Python and PySpark. Candidates must have proven experience designing enterprise data solutions within the Microsoft Azure ecosystem.
Full description
Be Part Of A High-Performing Team:
Join a global financial technology organization modernizing the data capabilities that support broker-dealer and capital markets operations. This team is developing a strategic, cloud-based data platform designed to improve how securities, pricing, reference, and market data are governed and consumed across the enterprise. The environment brings together experienced engineers and financial technology professionals working across the United States and India, with a strong emphasis on collaboration, scalable architecture, disciplined development standards, and data-driven innovation.
What's In Store For You:
Engagement: W2 only (no C2C/1099)
This is a long-term hybrid consulting opportunity based in Charlotte, North Carolina. The role provides direct involvement in a large-scale digital transformation and the development of an enterprise data platform on Microsoft Azure. The selected engineer will gain exposure to complex capital markets data, modern cloud engineering practices, distributed development teams, and strategic initiatives with significant organizational visibility.
How You Will Make An Impact:
- Design, develop, and enhance a strategic enterprise data platform supporting capital markets and securities operations.
- Build scalable data pipelines and processing solutions using Python, PySpark, Azure Data Factory, Azure Databricks, and Azure Data Lake Storage Gen2.
- Support the initial implementation of securities reference data and pricing data capabilities before expanding the platform into additional data domains.
- Develop Python-based APIs using FastAPI or comparable frameworks to make trusted data available to downstream applications and consumers.
- Integrate Azure databases, API management, Azure Functions, and related services into reliable end-to-end data solutions.
- Apply strong SQL and data modeling expertise across relational and NoSQL database environments.
- Contribute to CI/CD pipelines, version control, automated deployments, and established enterprise development standards.
- Collaborate with technology and business stakeholders across Charlotte, Jersey City, and India to deliver consistent, production-ready solutions.
Requirements
Do You Bring Proven Success in Azure Data Engineering and Python Development?
- 10 or more years of relevant software development or data engineering experience.
- Advanced hands-on development experience with Python and PySpark.
- Proven experience designing and implementing enterprise data solutions in Microsoft Azure.
- Strong working knowledge of Azure Data Factory, Azure Data Lake Storage Gen2, Azure Databricks, Azure databases, and Azure Functions.
- Experience with Microsoft Fabric or comparable modern cloud data platform capabilities.
- Experience developing REST APIs using FastAPI or a similar Python framework.
- Strong SQL expertise with relational database management systems and/or NoSQL databases.
- Solid understanding of ETL and ELT architecture, data integration, data transformation, and production data pipelines.
- Familiarity with API gateway and API management capabilities.
- Experience with Git, Jenkins, CI/CD processes, and the broader DevOps lifecycle.
- Ability to follow enterprise engineering standards and collaborate effectively across distributed teams.
- Financial services experience is preferred, particularly involving capital markets, financial instruments, asset classes, securities reference data, pricing data, or market data.
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