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# Lead Data Engineer

- **Company:** Selby Jennings
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Big Data, Code Review, Computer Programming, Databases, Data Architecture, Data Infrastructure, Data Integrity, Data Systems, Data Warehousing, Python (Programming Language), Meta-Data Management, NoSQL, Service-Oriented Architecture, SQL Databases, Workflow Management Systems, Cloud Platform System, Large Language Models, Snowflake, IT Architecture, Backend, Data Management
- **Published:** August 22, 2026
- **Apply:** https://www.efinancialcareers.com/jobs-United_States-Manhattan-Lead_Data_Engineer.id24692327

## About the Role

Opportunity to mentor and potentially lead a growing team of engineers. Qualifications Experience building and supporting enterprise-scale data platforms and pipelines. Strong programming skills in Python and SQL. Expertise with Snowflake or a similar modern cloud data warehouse. Hands-on experience designing and deploying solutions within AWS. Experience with workflow orchestration tools such as Airflow. Knowledge of relational and NoSQL database technologies. Strong understanding of data modeling, data architecture, and large-scale data processing. Proven ability to lead technical initiatives and deliver complex projects. Preferred Qualifications Experience with Go or other backend development languages. Background in platform engineering or service-oriented architectures. Exposure to AI/LLM-powered data solutions and automation. Experience within financial services, investment management, trading, or market data environments. Track record of building highly reliable and scalable data
systems.

## Description

Responsibilities Lead the development of cloud-based data platforms, pipelines, and data products leveraging Python, SQL, Snowflake, and AWS. Build scalable solutions for a variety of data sources, including market, alternative, and enterprise datasets. Enhance the firm's data platform through APIs, automation, metadata management, and emerging AI-driven capabilities. Improve data reliability through quality controls, monitoring, testing, and operational best practices. Troubleshoot complex data issues and implement long-term solutions to improve platform stability. Design efficient data models and datasets that support analytics, reporting, and quantitative research. Collaborate with investment, research, and technology teams to deliver scalable data solutions. Provide technical leadership through architecture guidance, code reviews, mentorship, and engineering best practices. Drive projects from design through production while influencing the future direction of the data platform.


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