Organization and Team
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). The RSG heads the Office of Investment Management (OIM), which manages a $100+ billion multi-asset class, global investment portfolio, with about 85% of the assets actively managed in-house. The portfolio includes global equities, fixed income, foreign exchange, private equity, real estate, infrastructure, timber, and commodities. OIM staff are based in New York and represent over 40 countries (https://oim.unjspf.org/).
These roles are part of the Business Intelligence Team, Data and AI Unit. The incumbents will work under the technical direction of the Data Engineer (P-4) and will report to the Chief of Data until the P-4 is onboarded. The team supports OIM’s data strategy, enterprise data platform and lakehouse, analytics infrastructure, reporting capabilities, and data governance frameworks for investment and risk management decision-making.
Key Responsibilities
- Design and develop solutions for data-related technical problems and data infrastructure needs identified with business, investment, risk, analytics, and technology stakeholders.
- Build, test, deploy, operate, and improve production data pipelines and platform components that integrate 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 help 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 and 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.
- Use AI-assisted engineering tools and specialized agents, where appropriate, to accelerate and standardize activities across the data product lifecycle, applying appropriate human review, testing, security, and governance controls.
- 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; ability to test and quality-check own work prior to release; process-oriented approach with strong documentation skills; ability to explain technical work and progress to clients in a relatable manner; understanding of the impact of data-driven and evidence-based decisions; sound judgment, ability to plan own work, and manage conflicting priorities; pride in work and achievements; conscientious and efficient meeting of commitments and deadlines; persistence with difficult problems; calm under stress; responsibility for incorporating gender perspectives and ensuring equal participation of women and men.
- Teamwork: Collaborative approach to achieve organizational goals; solicits input by valuing others’ ideas and expertise; willing to learn from others; places team agenda before personal agenda; supports and acts in accordance with final group decisions, even when they differ from personal position; shares credit and accepts joint responsibility.
- Planning and Organizing: Develops goals aligned with agreed strategies; identifies priority activities and assignments; adjusts priorities as needed; allocates appropriate time and resources; anticipates risks and plans contingencies; monitors and adjusts plans and actions; uses time efficiently.
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) plus two additional years of qualifying experience may be accepted in lieu of the advanced university degree.
- A professional certification in a cloud data platform is desirable, such as SnowPro Core or SnowPro Advanced (Snowflake), Databricks Certified Data Engineer, or an equivalent data engineering certification from AWS or Azure.
Work Experience
- Minimum of five years of progressively responsible data engineering experience, 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 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: Minimum of two years 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, to deliver and support production data products.
- Data modelling and governance: Skills in data modelling and database design, with 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 minimum required level for each skill (reading, writing, listening, speaking) is UN Level II (per the UN Language Framework).
Assessment
Evaluation of qualified candidates may include a hands-on technical assessment, which may be followed by a competency-based interview.
Special Notice
- This temporary job opening is 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 sub
