Data Engineer

Data Engineer (Two Positions), P3 (Temporary Job Opening No. 286310)- Deadline to apply: 25 October 2026

Job Summary

Join the United Nations Joint Staff Pension Fund (UNJSPF) as a Data Engineer and contribute to the development of modern data infrastructure supporting a global investment portfolio valued at over $100 billion. The Office of Investment Management (OIM) manages a diversified portfolio across multiple asset classes, with approximately 85% of assets actively managed in-house. Based in New York, OIM brings together investment and technology professionals from more than 40 countries.

These positions are located in the Business Intelligence Team, Data and AI Unit of OIM. The Data Engineers work under the technical direction of the Data Engineer (P-4) and report to the Chief of Data until the P-4 is onboarded. The team is responsible for OIM's data strategy, enterprise data platform and lakehouse, analytics infrastructure, reporting capabilities, and data governance frameworks.

The incumbents design, develop, and maintain production data pipelines, enhance enterprise data platforms, and support data integration, governance, and analytics capabilities. The roles provide an opportunity to contribute to cloud data engineering, enterprise data architecture, DevOps, AI-assisted engineering, and data-driven investment and risk management within a diverse and international environment.

Job Responsibilities

Responsibilities include but are not limited to:

• Design and develop technical solutions to address data infrastructure needs and data-related challenges in collaboration with business, investment, risk, analytics, and technology stakeholders.

• Build, test, deploy, operate, and enhance production data pipelines integrating structured and unstructured data from multiple sources into the enterprise data platform and lakehouse.

• Develop data management tools and solutions to support analytics, quantitative research, and data science teams in building and optimizing data products.

• Apply software development lifecycle and DevOps practices, including version control, code review, automated testing, continuous integration and deployment, and monitoring.

• Monitor and maintain production data pipelines and platform components, troubleshoot technical issues, conduct incident response and root-cause analysis, and implement improvements.

• Develop and implement enhancements to data systems to ensure compliance with data governance, data protection, privacy, and security requirements.

• Collaborate with relevant stakeholders to strengthen data quality, governance, security controls, and the reliability of enterprise data products.

• Develop and maintain technical and business documentation, including requirements, data models, data contracts, lineage, metadata, testing evidence, operating procedures, and decision records.

• Utilize AI-assisted engineering tools and specialized agents, where appropriate, to accelerate and standardize activities throughout the data product lifecycle.

• Apply appropriate human review, testing, security, and governance controls to ensure the reliable use of AI-assisted engineering solutions.

Required Qualifications, Capabilities, and Skills

• An advanced university degree (Master's degree or equivalent) in computer science, software engineering, data science, analytics, statistics, applied mathematics, information management, data management, information systems, information science or a related quantitative field. A first-level university degree (Bachelor’s degree or equivalent) in combination with two additional years of qualifying experience may be accepted in lieu of the advanced university degree.

• A professional certification in a cloud data platform, such as SnowPro Core or SnowPro Advanced (Snowflake), Databricks Certified Data Engineer, or an equivalent data engineering certification from AWS or Azure, is desirable.

• A minimum of five years of progressively responsible experience in data engineering, including building, deploying and operating production data pipelines on a cloud data platform or lakehouse, is required.

• Production data pipeline engineering: Advanced SQL and Python skills, with the ability to design, code, test, deploy, troubleshoot and operate production ETL and ELT pipelines on a cloud data platform or lakehouse, integrating structured and unstructured data from multiple sources is required

• Software development lifecycle and DevOps: A minimum of two years of hands-on experience applying software development lifecycle and DevOps practices to data products in an Agile delivery environment, including Git-based version control, code review, automated testing, continuous integration and deployment, and monitoring is required.

• Ability to use tools such as Azure DevOps, dbt and Airflow, or similar, to deliver and support production data products is required.

• Data modelling and governance: Skills in data modelling and database design, and the ability to apply data quality, security and privacy requirements when building or changing data systems are required.

• Hands-on experience building and operating data solutions on Snowflake is desirable.

• Experience with Databricks or a comparable cloud data platform is desirable.

• Experience using AI-assisted engineering tools or agents in data engineering work is desirable.

Benefits

• Opportunity to contribute to enterprise data platforms and technology modernization within a global pension fund managing a $100+ billion investment portfolio .

• Work with a diverse, international team of investment and technology professionals representing more than 40 countries.

• Contribute to cloud data engineering, lakehouse technologies, data integration, DevOps, AI-assisted engineering, and enterprise data governance supporting investment and risk management.

• Gain experience working with modern data technologies and supporting enterprise-wide analytics and data-driven decision-making.