Data Engineer (Two Positions)
Organizational Context
The United Nations Joint Staff Pension Fund (UNJSPF) Office of Investment Management (OIM) in New York manages a global investment portfolio exceeding $100 billion. The Business Intelligence Team, within the Data and AI Unit, leads OIM's data strategy, enterprise data platform, analytics infrastructure, and reporting capabilities to support investment and risk management decisions.
Job Purpose
The Data Engineer will design, develop, and implement solutions for data infrastructure needs and technical data problems. This role involves building, testing, deploying, and optimizing production data pipelines and platform components to integrate diverse data sources into the enterprise data platform and lakehouse. The incumbent will develop data management tools for analytics and data science teams, apply software development lifecycle and DevOps practices for reliable data products, and ensure compliance with data governance, protection, privacy, and security requirements. The position also requires creating comprehensive technical documentation and leveraging AI-assisted engineering tools to enhance efficiency and standardization across the data product lifecycle, contributing to informed investment and risk management decisions.
Responsibilities
Design and develop data solutions and infrastructure to address technical problems, collaborating with business, investment, risk, analytics, and technology stakeholders. Build, test, deploy, operate, and enhance production data pipelines and platform components, integrating structured and unstructured data into the enterprise data platform and lakehouse. Develop data management tools to support analytics, quantitative, and data science teams in optimizing data products. Apply software development lifecycle and DevOps practices, including version control, code review, automated testing, CI/CD, monitoring, and incident response, to ensure reliable and maintainable data products. Implement changes to data systems to comply with data governance, protection, privacy, and security requirements. Create and maintain clear technical and business documentation, including requirements, data models, lineage, and operating procedures. Utilize AI-assisted engineering tools to accelerate and standardize data product lifecycle activities, ensuring appropriate human review and governance.
Work Experience
A minimum of five years of progressive experience in data engineering, including building, deploying, and operating production data pipelines on cloud platforms or lakehouses, is required. This includes advanced SQL and Python skills for ETL/ELT pipelines. At least two years of experience applying software development lifecycle and DevOps practices in an Agile environment is also required. Experience with data modeling, database design, and data governance is necessary. Hands-on experience with Snowflake, Databricks, or similar platforms, and with AI-assisted engineering tools, is desirable.
Skills
Professionalism, Teamwork, Planning and Organizing, Data Engineering Lifecycle Knowledge, Analytical Skills, Problem-Solving, Troubleshooting, Documentation Skills, Communication, Judgment, Time Management, Software Development Lifecycle, DevOps Practices, Data Modeling, Data Governance.