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

International Monetary Fund•Washington, DC

23h

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

Provides advanced engineering expertise in coordination with solution owners, product owners, project managers, enterprise architects, technical leads, data owners, security stakeholders, and platform administrators to enhance and modernize economic data systems while adhering to enterprise architecture, governance, security, and technology policies.
Designs, implements, enhances, and operates economic data systems across acquisition, ingestion, transformation, storage, quality validation, semantic modeling, visualization, dissemination, monitoring, and lifecycle management.
Applies general architecture patterns for governed economic data solutions, including reusable data products, domain-oriented data models, environment separation, curated data layers, secure integration boundaries, metadata-driven design, and operational support models.
Engineers source onboarding and data integration patterns, including batch, streaming, change data capture, file-based exchange, API integration, schema onboarding, data contracts, metadata capture, ingestion error handling, and secure credential and connectivity practices.
Develops data transformation, storage, and processing solutions using SQL, Python, cloud-native data services, lakehouse or warehouse platforms, NoSQL and search technologies, and enterprise analytics tools as appropriate to the business and technical requirements.
Implements data quality engineering controls including validation rules, reconciliation logic, freshness and completeness checks, anomaly thresholds, quality dashboards, exception handling, and evidence that data is fit for downstream consumption and promotion.
Implements DataOps and DevOps practices including source control, automated testing, CI/CD, controlled Dev/Test/Prod promotion, deployment documentation, release checklists, rollback planning, production readiness reviews, and lifecycle controls.
Configures, manages, monitors, and tunes installed economic data systems, data platforms, pipelines, semantic models, access controls, capacities, and associated infrastructure to ensure availability, scalability, performance, reliability, security, and cost-effective production operations.
Performs technical investigation, incident triage, root-cause analysis, remediation, and post-incident prevention for failures, data quality issues, performance anomalies, SLA or OLA breaches, and operational defects affecting economic data products and systems.
Implements governance and compliance controls including metadata completeness, lineage, classification, access management, privacy protections, privileged access controls, audit evidence, retention considerations, data stewardship support, and exception tracking in accordance with enterprise standards.
Supports AI, machine learning, data science, semantic search, retrieval-augmented generation, forecasting, and AI-assisted economic research by engineering trusted, traceable, well-governed, secure, and reusable economic data assets; advises application development, analytics, and data engineering teams on economic data standards, SDMX, semantic definitions, tools, and reusable engineering patterns; and provides guidance to junior colleagues as needed.
Ensures platform’s high availability. Monitors system performance, uptime, and resolves major technical escalations. Oversees infrastructure costs, storage limits, and service provider performance
Acts as the bridge between technical engineers and business stakeholders. Manages the platform roadmap and feature backlog for platform enhancements.