At the International Finance Corporation (IFC), a member of the World Bank Group (WBG), you’ll join a diverse, global community working across cultures, disciplines, and borders to address the world’s most pressing development challenges. With 1.2 billion young people reaching working age in the decade ahead, the challenge of job creation has never been greater. Through partnerships across 145 countries and more than 182 offices worldwide, we work with governments, private sector, development partners and other stakeholders to invest in people, strengthen markets, and deliver scalable, data-driven solutions that generate more and better jobs and improve lives. In fiscal year 2026, IFC committed a record $105.6 billion to private companies and financial institutions in developing countries. For more information, visit www.ifc.org. The Corporate Information Technologies Department enables IFC’s strategic priorities and operations through secure, reliable technology solutions. CIT partners with the business to automate processes, improve responsiveness and efficiency, manage risk, and strengthen data, analytics, information, knowledge, and learning capabilities. CITDE is IFC’s data solutions organization, building trusted data foundations, scalable platforms, and data products that power decisions, analytics, AI, and operational excellence. We combine modern engineering, product thinking, and business impact to solve institution-wide challenges, accelerate innovation, and create measurable value across global development programs. We are looking for an experienced Data Engineer to lead the design, delivery, and evolution of trusted data products that support critical business decisions across IFC. This is a senior individual contributor role that combines deep, hands-on data engineering expertise with meaningful data product ownership responsibilities. The role is anchored in data engineering with some elements of product management. The successful candidate will clarify business intent, shape priorities, define acceptance criteria, make informed trade-offs, and remain accountable for the reliability, adoption, cost, and continuous improvement of assigned data products. The role also requires hands-on use of AI-enabled engineering practices and a willingness to keep experimenting, learning, and helping the team adopt effective approaches responsibly. Candidates should be able to show how they already use AI assistants or agents in real engineering work, in their current role or in their own projects, and how they check the results. Extensive experience building agentic workflows is not required, but candidates should demonstrate sound judgment, active learning, and openness to changing how data products are designed, built, tested, released, and operated. Data products in this team serve two kinds of consumers: people, through reports and analysis, and AI agents and natural-language tools that query data directly. The role builds for both. What success looks like in the first 12 months • Own at least one data product end to end, with a published contract, quality rules, defined users and measured adoption. • Move at least one workload off a legacy platform or pattern onto the Databricks lakehouse, using AI-assisted methods where they speed up the work. • Add reusable specifications, prompts, skills or patterns to the team's shared library, so other engineers can repeat what worked. • Show measurable improvement in the quality, freshness or cost of the products owned. Duties and accountabilities: Data Engineering and Architecture • Design, build, and maintain scalable, secure, and reliable batch and streaming data pipelines using Python, SQL, PySpark, and modern data processing frameworks. • Lead ETL and ELT solutions for structured, semi-structured, and unstructured data, including metadata enrichment and AI-ready preparation for analytics and GenAI use cases. • Develop logical and physical data models, curated datasets, and lakehouse layers optimized for analytics, reporting, operational use, and downstream consumption. • Establish and evolve data contracts covering schemas, definitions, ownership, service levels, quality expectations, and change-management arrangements. • Set technical direction for assigned data products and ensure alignment with approved enterprise architecture patterns and standards. • Analyze schema changes and downstream impacts, align solutions with master and reference data, and guide decisions on versioning and migration. • Review designs and code, resolve complex engineering issues, and ensure solutions are reusable, maintainable, and fit for production. • Modernize legacy data workloads (for example Oracle Exadata, Informatica, and Azure Data Factory pipelines) onto the Databricks lakehouse, including analysis of existing logic, reconciliation of old and new outputs, and controlled cutover. Use AI tools to speed up code analysis, conversion, and test generation for this work. • Apply modern engineering practices on Databricks, including Git-based development, Databricks Asset Bundles or equivalent infrastructure-as-code, Lakeflow pipelines and jobs, and automated testing. Data Product Ownership • Translate business needs and intended decisions into clear data product outcomes, requirements, and acceptance criteria. • Define the product's users, expected use, service levels, quality thresholds, and measurable indicators of adoption and value. • Develop and maintain a prioritized backlog with business owners, consumers, architects, and delivery teams. • Own the data product across its lifecycle, from source discovery and design through release, adoption, operation, improvement, and retirement. • Make informed trade-offs among scope, value, quality, delivery timing, technical debt, cost, and operational risk. • Use consumer feedback, usage information, production incidents, quality results, and operating costs to prioritize improvements. • Communicate product decisions, progress, risks, dependencies, and outcomes clearly to technical and non-technical stakeholders. • Register and maintain owned data products in the enterprise data product inventory (Collibra), and take them through the IFC data product framework's review stages. • Work with business analytics teams who own metric meaning (for example COMAR) to encode agreed business definitions and shared metrics consistently in the semantic layer. AI-Enabled Data Engineering Lifecycle • Clarify the decision each data product serves, identify intended consumers, define success measures, and confirm acceptance criteria before build begins. • Lead source discovery and acquisition by identifying systems of record, profiling data, documenting grain and source behavior, and coordinating access and data-use approvals. • Use approved AI tools to support modeling, pipeline development, testing, documentation, release preparation, monitoring, incident analysis, and consumer support where appropriate. • Determine what work should remain human-led, what can be AI-augmented, and what may be automated within approved controls. • Critically review and validate AI-generated outputs, retaining accountability for technical quality, security, privacy, data use, and production outcomes. • Capture source behavior, definitions, contracts, design decisions, quality rules, reviewer corrections, incidents, and root causes as reusable organizational context. • Experiment frequently and responsibly with emerging AI-enabled engineering practices, evaluate results on live work, and share effective approaches and lessons with colleagues. • Write specifications, acceptance tests, and instructions clear enough for AI agents to execute, and maintain them as reusable team assets (for example prompt libraries, agent instructions, and skills). • Make data products usable by AI agents and natural-language tools by maintaining business descriptions, metric definitions, and semantic metadata in Unity Catalog, and by preparing and maintaining spaces for tools such as Databricks Genie. • Design and run evaluations for AI-generated outputs that touch data products, such as natural-language query answers or AI-based extraction, using reference question sets, expected results, and tracked accuracy over time. Performance, Governance, and Operational Excellence • Optimize data processing through effective partitioning, storage, caching, indexing, orchestration, and workload design. • Monitor and improve reliability, freshness, service levels, performance, and cost for assigned data products. • Implement data lineage, classification, access control, auditing, and other governance capabilities using platforms such as Unity Catalog and Collibra. • Ensure adherence to enterprise standards for privacy, security, responsible AI, risk management, and separation of duties. • Establish automated quality controls, reconciliation, test coverage, deployment gates, rollback plans, and production-readiness evidence. • Lead incident triage and root-cause analysis for complex issues, convert lessons into preventive controls, and guide decisions on remediation or retirement. • Use continuous integration and deployment practices to promote changes safely across environments. • Use data observability tools (for example Monte Carlo) to monitor freshness, volume, schema, and quality, and connect alerts to clear ownership and response. • Track and attribute compute and storage cost for owned products, and apply cost controls in line with the team's FinOps practices. Collaboration and Technical Leadership • Partner with business stakeholders, data owners, data scientists, analysts, architects, security specialists, and platform teams to deliver usable and trusted data products. • Facilitate decisions on definitions, sourcing, design, quality thresholds, release readiness, and residual risk. • Provide technical leadership and mentorship to engineers, including design guidance, code review, problem solving, and knowledge sharing. • Contribute reusable patterns, standards, and practices that improve consistency and delivery across teams. • Present complex technical choices and recommendations clearly to senior technical and business stakeholders. • Help colleagues adopt AI-enabled engineering practices through pairing, demonstrations, and shared examples from live work.