Senior Python Data Engineer for Quant Analytics (Finance)

ZealHire Inc.; New York, Northern (NY, KY); Hybrid; USD 180,000 - 260,000; Full time

Job summary

ZealHire Inc. in New York is seeking a highly experienced Python Data Engineer / Quant Developer with 15+ years of professional experience. You will apply strong Python, SQL, and data-engineering expertise to design scalable, data-driven solutions for financial-services environments.

The role involves building robust data pipelines, optimizing large-scale data processing, and collaborating with analysts, developers, and business teams to deliver reliable analytics and risk-focused workflows.

Qualifications

• 15+ years of IT experience.
• Strong hands-on Python development.
• Advanced SQL skills.
• Strong Data Engineering experience.
• Experience with large-scale datasets.
• Excellent analytical and problem-solving abilities.
• Excellent communication and collaboration skills.

Responsibilities

• Design and develop scalable data-processing solutions using Python and SQL.
• Build and maintain data pipelines and analytical workflows.
• Work with large and complex datasets for quantitative and business analysis.
• Perform data transformation, cleansing, validation, and reconciliation.
• Develop reusable Python components for data-intensive applications.
• Optimize SQL queries and data-processing performance.
• Troubleshoot production data and application issues.
• Collaborate with quantitative analysts, developers, data engineers, and business teams.
• Support testing, deployment, documentation, and production activities.

Skills

Python development

SQL

Data engineering

Large-scale datasets

Analytical problem solving

Communication skills

Job description

ZealHire Inc. in New York is seeking a highly experienced Python Data Engineer / Quant Developer with 15+ years of professional experience. You will apply strong Python, SQL, and data-engineering expertise to design scalable, data-driven solutions for financial-services environments.

The role involves building robust data pipelines, optimizing large-scale data processing, and collaborating with analysts, developers, and business teams to deliver reliable analytics and risk-focused workflows.