About this role
Digital Factory - Data Engineer - Associate Location: Luxembourg Other locations: Primary Location Only Salary: Competitive Job description Requisition ID: 1685505 At EY, we're all in to shape your future with confidence. We'll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world. Will you shape the future or will the future shape you? Here at EY, you'll have the chance to build a truly exceptional experience. We'll empower you with the latest technology, surround you with high-performing teams, and provide the global scale and diverse and inclusive culture you need to discover your full potential. Through our coaching and training programs, you'll develop the skills you need to stay relevant today and, in the future, – all while building a network of colleagues, mentors, and leaders who will be on the journey with you at EY and beyond. The opportunity: your next adventure awaits The Data Engineer designs, builds, and operates scalable, reliable, and secure data platforms and pipelines that power EY's digital products, analytics, and data driven use cases. The role focuses on engineering robust data foundations—including ingestion, transformation, storage, and access—using modern data engineering practices. You will collaborate closely with Backend Engineers, Analytics, Data Science, Product, and Architecture teams to deliver trusted, high quality data assets in an enterprise and regulated environment. Responsibilities Data Platform & Pipeline Engineering • Design, implement, and maintain batch and streaming data pipelines using Python and modern data frameworks. • Build reliable ingestion and transformation processes from internal and external data sources (APIs, databases, files, events). • Develop reusable data processing components and frameworks following software engineering best practices. • Ensure clear data contracts, schemas, versioning, and documentation. Data Quality, Reliability & Observability • Implement data validation, quality checks, and monitoring to ensure accuracy, completeness, and timeliness. • Write clean, testable code with unit and integration tests for data workloads. • Instrument pipelines with logging, metrics, and alerts; define SLAs/SLOs for critical data products. • Troubleshoot and resolve data incidents using root cause analysis and durable fixes. Performance & Scalability • Optimize data processing performance, storage layouts, and query efficiency. • Design pipelines for scalability and resilience (retries, idempotency, checkpointing, backpressure). • Apply partitioning, parallelization, and caching strategies where appropriate. Architecture & Collaboration • Contribute to data platform architecture, including lakehouse, warehouse, and event driven designs. • Collaborate with Backend Engineers to align APIs and services with data consumption patterns. • Work closely with Analytics, Data Science, and Product teams to translate requirements into reliable data solutions. • Document architecture decisions, data models, and operational runbooks. Security, Privacy & Compliance • Implement secure data handling practices, including access controls, encryption, and secrets management. • Ensure compliance with enterprise, regulatory, and privacy requirements (data classification, retention, lineage). • Support audits and data governance processes. Operations & Continuous Improvement • Own data pipelines in production: monitor, maintain, and continuously improve reliability and performance. • Reduce operational toil through automation and standardized frameworks. • Contribute to shared data engineering standards, templates, and best practices across the Digital Factory. What we look for: • You have an agile, growth-oriented mindset. What you know matters. But the right mindset is just as important in determining success. We're looking for people who are innovative, can work in an agile way and keep pace with a rapidly changing world. • You are curious and purpose driven. We're looking for people who see opportunities instead of challenges, who ask better questions to seek better answers. • You are inclusive. We're looking for people who seek out and embrace diverse perspectives, who value differences, and team inclusively to build safety and trust. Qualifications Required • Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or a related field. • Professional experience as a Data Engineer or in a strongly data focused engineering role. • Strong experience with Python for data engineering use cases. • Solid experience with SQL and data modeling concepts (dimensional, normalized, or lakehouse patterns). • Hands on experience with relational and analytical data stores (e.g. PostgreSQL, SQL Server, data warehouses, data lakes). • Experience with CI/CD, automated testing, and code reviews for data workloads. • Working knowledge of cloud platforms (Azure preferred) and data services. • Familiarity with containerization (Docker) and orchestration concepts. • Strong understanding of data security, access control, and observability. Preferred • Experience with modern data frameworks and platforms (e.g. Spark, Airflow, dbt, Azure Data Factory, Databricks, Microsoft Fabric). • Exposure to streaming and event based data processing (e.g. Kafka, Azure Event Hubs, Service Bus). • Experience with lakehouse or enterprise data warehouse architectures. • Background in financial services or regulated environments. • Familiarity with infrastructure as code (Terraform/Bicep). • Experience working in distributed, cross functional teams. What's in it for you: • Develop, implement, and continuously improve delivery governance standards, templates, and tooling across the Digital Factory. • Coordinate portfolio-level reporting, risk tracking, and delivery health metrics to provide clear visibility of project status and performance. •