10+ years of software development or data engineering experience
Advanced proficiency in Python and PySpark
Experience designing enterprise data solutions in Microsoft Azure
Strong knowledge of Azure Data Factory, Data Lake Storage Gen2, and Databricks
Familiarity with Microsoft Fabric or similar cloud data platforms
Experience developing REST APIs using FastAPI or similar frameworks
Strong SQL skills with relational and NoSQL databases
Understanding of ETL/ELT architecture and data integration processes
Responsibilities
Design and enhance an enterprise data platform for capital markets
Build scalable data pipelines using Python, PySpark, and Azure tools
Support implementation of securities reference and pricing data capabilities
Develop Python-based APIs for data accessibility
Integrate Azure services into reliable data solutions
Contribute to CI/CD pipelines and automated deployments
Collaborate with stakeholders across multiple locations for production-ready solutions
Benefits
Long-term hybrid consulting opportunity
Exposure to large-scale digital transformation projects
Involvement in complex capital markets data initiatives
Opportunity to work with distributed development teams
Significant organizational visibility and impact on strategic initiatives
Full Job Description
Job Description 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.
