Org. Setting and Reporting
The United Nations Secretary-General is responsible for investing the assets of the United Nations Joint Staff Pension Fund (UNJSPF). This responsibility has been delegated to the Representative of the Secretary-General for the investment of the assets of the UNJSPF (RSG), who leads the Office of Investment Management (OIM).
OIM manages a $100+ billion multi-asset class, global investment portfolio, with approximately 85% actively managed in-house. Asset classes include global equities, fixed income, foreign exchange, private equity, real estate, infrastructure, timber, and commodities. OIM staff are based in New York and come from over 40 countries.
These two positions are located in the Business Intelligence Team, Data and AI Unit. The incumbents will 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 leads OIM’s data strategy, enterprise data platform and lakehouse, analytics infrastructure, reporting capabilities, and data governance frameworks to support investment and risk management decision-making.
Responsibilities
- Design and develop solutions for data-related technical problems and data infrastructure needs in partnership with business, investment, risk, analytics, and technology stakeholders.
- Build, test, deploy, operate, and improve production data pipelines and platform components integrating structured and unstructured data from multiple sources into the enterprise data platform and lakehouse.
- Develop data management tools for analytics, quantitative, and data science teams to build and optimize data products.
- Apply the software development lifecycle and DevOps practices, including version control, code review, automated testing, continuous integration and deployment, monitoring, incident response, and root-cause analysis to deliver reliable, maintainable data products.
- Develop and implement changes to data systems to ensure compliance with data governance, protection, privacy, and security requirements, in collaboration with key stakeholders.
- Create and maintain clear technical and business documentation alongside the code, including requirements, data models, data contracts, lineage, metadata, testing evidence, operating procedures, and decision records to support understanding, governance, maintenance, and extension of data products.
- Use AI-assisted engineering tools and specialized agents where appropriate to accelerate and standardize activities across the data product lifecycle, with appropriate human review, testing, security, and governance controls to ensure reliable delivery.
- Perform other assigned duties.
Competencies
- Professionalism: Knowledge of the data engineering lifecycle from source integration and transformation through production delivery and consumption. Strong analytical skills with structured and unstructured datasets. Excellent problem-solving and troubleshooting skills, including the ability to test and quality-check work before release. Process orientation and strong documentation skills. Ability to explain technical work and progress in a relatable manner. Understanding of the impact of enabling data-driven, evidence-based decisions. Ability to apply judgment, plan own work, and manage conflicting priorities. Demonstrates pride in work, professional competence, conscientiousness, efficiency, commitment to deadlines, persistence in difficult situations, and calm under stress. Takes responsibility for incorporating gender perspectives and ensuring equal participation of women and men in all areas of work.
- Teamwork: Collaborates to achieve organizational goals, values others’ ideas and expertise, is willing to learn, places team agenda before personal agenda, supports group decisions even when they differ from one’s own position, shares credit for team accomplishments, and accepts joint responsibility.
- Planning and Organizing: Sets clear goals aligned with agreed strategies, identifies priority activities, adjusts priorities as needed, allocates appropriate time and resources, foresees risks and plans contingencies, monitors and adjusts actions, and uses time effectively.
Education
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) combined 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.
Work Experience
- 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.
- 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.
- Software development lifecycle and DevOps: At least 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.
- Ability to use tools such as Azure DevOps, dbt, and Airflow, or similar tools, to deliver and support production data products.
- 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.
- 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.
Languages
English and French are the working languages of the United Nations Secretariat. For this job opening, English is required. The required level for English is UN Level II for reading, writing, listening, and speaking.
Assessment
Evaluation of qualified candidates may include a hands-on technical assessment, which may be followed by a competency-based interview.
Special Notice
- This is a temporary job opening for two positions available for a period of up to 364 days each and is subject to GA approval of the 2027 budget.
- Further extensions are subject to approval.
