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Hudson Information Technology and Manpower Services

Data Engineer – Capital Markets

Posted 12 Days Ago
In-Office
New York City, NY, USA
50-55 Hourly
Senior level
In-Office
New York City, NY, USA
50-55 Hourly
Senior level
Designs and maintains scalable ETL/ELT pipelines, data warehouses, lakes, and models for analytics, business intelligence, and AI/ML initiatives. Ensures data quality, governance, integrity, and security while optimizing SQL and processing performance. Collaborates with analysts, data scientists, and application teams to support Capital Markets, Fraud Screening, and Financial Crimes use cases. Requires strong Python, Snowflake, SQL, cloud platform, data integration, and financial domain expertise. The role is onsite in New York, NY.
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Job Description

We are seeking a skilled Data Engineer with Capital Markets experience to design, build, and optimize scalable data pipelines supporting business intelligence, analytics, and AI initiatives.

The ideal candidate will have strong expertise in data engineering, data integration, data modeling, cloud data platforms, Fraud Screening, and Financial Crimes.

Responsibilities

  • Design, develop, and maintain robust ETL/ELT data pipelines.

  • Build and optimize large-scale data warehouses and data lakes.

  • Develop data models supporting reporting, analytics, and operational requirements.

  • Ensure data quality, integrity, governance, and security across platforms.

  • Work with structured and unstructured data from multiple sources.

  • Collaborate with business analysts, data scientists, and application teams.

  • Optimize SQL queries and data processing for performance and scalability.

  • Implement cloud-based data solutions using AWS, Azure, or GCP.

  • Automate data workflows, monitoring, and data quality processes using modern data engineering tools.

  • Support AI/ML initiatives by providing reliable and high-quality datasets.

  • Work with teams supporting Capital Markets, Fraud Screening, and Financial Crimes use cases.

Required Skills

  • 8–10 years of Data Engineering experience.

  • Strong Capital Markets domain experience.

  • Experience with Fraud Screening and Financial Crimes.

  • Strong Python skills.

  • Strong Snowflake experience.

  • Hands-on experience with ETL/ELT pipelines and data integration.

  • Strong SQL and data modeling experience.

  • Experience with cloud platforms such as AWS, Azure, or GCP.

  • Experience with data warehouses and data lakes.

  • Strong understanding of data quality, governance, and security.

  • Willingness to work onsite in New York, NY.

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