Economic Data System Engineer/Sr. Economic Data System Engineer-ITDDPED

imf; Washington (District of Columbia); On-site; USD 150,000 - 210,000; Full time

Job summary

IMF is seeking a Senior/Economic Data Systems Engineer to design, implement, modernize, and operate enterprise-grade economic data systems across the full data lifecycle at Fund-wide scale.

The role collaborates with economists, product teams, governance and security groups to ensure secure, scalable, and governed data platforms that support analytics, reporting and AI-enabled use cases.

Qualifications

• Bachelor's degree in computer science, data engineering, or related discipline with 4+ years of experience or a Master's degree with 2+ years of experience.
• Strong experience designing, implementing, and operating enterprise-scale data engineering systems across the data lifecycle.
• Hands-on proficiency in SQL and Python; experience with cloud-native services and data platforms (Fabric, Databricks, Snowflake, BigQuery).
• Knowledge of metadata, lineage, data quality, governance, and security in production data environments.
• Experience with CI/CD, source control, testing, and production release processes.
• Ability to communicate complex solutions to technical and non-technical stakeholders and document architectural decisions.

Responsibilities

• Designing and implementing economic data systems and reusable data products with metadata-driven engineering and data contracts.
• Engineering data solutions using SQL, Python, Spark, Fabric, Databricks, and related tools.
• Applying data architecture, governance, and security principles across environments and platforms.
• Managing the data fabric platform's infrastructure, access controls, and platform roadmap.
• Preparing datasets for analytics, AI, forecasting, and retrieval-augmented generation, with documentation and traceability.

Skills

SQL

Python

Data modeling

ETL/ELT

Data governance

CI/CD

Education

Bachelor's degree in related field

Master's degree

Tools

Microsoft Fabric

Power BI

Databricks

Spark

Snowflake

BigQuery

Job description

Work for the IMF. Work for the World.

Under the direction of the Section Chief of Economic Data of ITD, the Senior/Economic Data Systems Engineer serves as an individual contributor. The position provides Fund-wide economic data engineering services supporting surveillance, lending, capacity development, research, analytics, reporting, and AI-enabled use cases. The incumbent designs, implements, modernizes, and operates economic data systems across the full data lifecycle, including source acquisition, ingestion, transformation, quality validation, metadata management, semantic modeling, dissemination, visualization, monitoring, and other data lifecycle engineering activities. The role focuses on engineering robust, secure, scalable, reusable, and governed economic data systems using advanced data engineering practices and contemporary data engineering technologies. The incumbent collaborates with economists, financial experts, data owners, product teams, application teams, enterprise architects, governance stakeholders, security teams, and platform administrators to assess requirements, design solutions, implement engineering patterns, and support reliable production operations.

Main responsibilities include:

• Designing and implementing economic data systems and reusable data products using advanced practices such as metadata-driven engineering, data contracts, schema evolution management, data observability, automated quality gates, CI/CD, DataOps, lineage management, semantic modeling, and lifecycle automation.
• Engineering data solutions/systems using technologies such as SQL, Python, Spark, Fabric, Databricks, Hadoop ecosystem tools, distributed processing frameworks, workflow orchestration platforms, cloud-native data services, lakehouse and warehouse platforms, API integration frameworks, NoSQL databases, search technologies, and enterprise analytics and visualization tools.
• Applying data and solutions architecture and governance principles, including separation of environments, controlled access, information classification, metadata completeness, lineage, data quality evidence, auditability, observability, privacy controls, and secure operational practices.
• Managing the overall technical infrastructure, availability, and access controls of the data fabric platform and specifically the economic data management platform. Setting the overarching platform vision, roadmap, and growth strategy as well as aligning platform features with strategic goals and compliance rules.
• Strengthening AI-readiness for economic data engineering capabilities by preparing trusted, traceable, well-documented, and secure datasets for advanced analytics, machine learning, forecasting, semantic search, retrieval-augmented generation, and AI-assisted data lifecycle development, automation, and optimization.

Minimum Qualifications

• Bachelor's degree in computer science, Computer Engineering, Software Engineering, Electrical Engineering, Information Systems, Data Engineering, or a related discipline plus ten (10) years of relevant professional experience, or Master's degree plus a minimum of four (4) years of relevant professional experience.
• Advanced experience designing, implementing, enhancing, and operating enterprise-scale data engineering systems, preferably supporting economic, financial, statistical, institutional, or time-series data domains. Strong knowledge of economic data, metadata and semantic models, SDMX, financial and economic metadata standards, and modern data architecture and engineering practices, including lakehouse and warehouse architectures, ELT/ETL, distributed processing, data quality, metadata, lineage, observability, governance, security, and production operations.
• Hands‑on proficiency in SQL and Python and experience with relevant data platforms, databases, APIs, orchestration tools, streaming technologies, cloud-native services, open data formats, search technologies, and analytics tools, such as Microsoft Fabric, Power BI, Databricks, Spark, Snowflake, BigQuery, and comparable technologies.
• Experience applying engineering and operational practices, including source control, automated testing, CI/CD, controlled environment promotion, release and rollback management, performance optimization, incident response, access controls, privacy protections, and auditability.
• Experience preparing trusted, secure, traceable, and reusable data for analytics, forecasting, AI, machine learning, semantic search, and retrieval solutions; combined with the ability to collaborate with technical and business stakeholders, document and explain complex solutions, guide colleagues, and remain current with evolving technologies and practices.
• Ability to collaborate with technical and non-technical stakeholders, assess requirements, produce architecture and operational documentation, explain complex concepts clearly, and provide technical guidance to colleagues as needed.

Duties and Responsibilities

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