Data Engineer (Two Positions)

The Data Engineer positions are responsible for designing, developing, and maintaining data infrastructure and production data pipelines to support the investment and risk management decision-making of the United Nations Joint Staff Pension Fund's Office of Investment Management. The roles involve working with structured and unstructured data, applying software development lifecycle and DevOps practices, and ensuring compliance with data governance and security requirements.

Responsibilities

• Design and develop solutions for data-related technical problems and data infrastructure needs identified in partnership 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 better build and optimize data products.
• Apply 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, so that data products can be understood, governed, maintained and extended.
• Use AI-assisted engineering tools and specialized agents, where appropriate, to accelerate and standardize activities across the data product lifecycle, while applying appropriate human review, testing, security and governance controls to ensure reliable delivery.
• Performs other assigned duties.

Requirements

• 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.
• 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.
• 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.
• 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.
• English is required at UN Level II for reading, writing, listening, and speaking.

Skills

• Data Engineering
• Cloud data platforms
• Snowflake
• Databricks
• SQL
• Python
• ETL pipeline design
• ELT Pipelines
• Software Development Lifecycle
• DevOps Practices
• Agile Development
• Git Versioning
• Code Reviews
• Automated Testing
• Continuous Integration
• Continuous Deployment
• Production Data Pipelines
• Data Modelling
• Database Design
• Data Quality Processes
• Data Security
• Data Privacy
• Azure DevOps
• dbt
• Airflow

Languages

English