Digital Factory - Senior Data Engineer - Senior Associate

Job description

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. Role Overview EY Luxembourg’s Digital Factory is looking for a Senior Data Engineer - Senior Associate to help design, build and improve reusable data and platform capabilities across consulting digital solutions. The role will initially support the Questionnaire and Survey Platform, which enables structured questionnaires, survey issuance, evidence collection, workflow tracking, reminders, review, approval and audit history. Over time, the successful candidate may also contribute to other Digital Factory initiatives requiring data modelling, pipeline engineering, integration, automation, auditability and operational reliability. This is a hands-on engineering role suited to a data engineer with solid Python, SQL, data modelling and production engineering experience. Prior exposure to Apache Airflow, graph database technologies or Azure platform services is beneficial, but not mandatory. For an internal hire, these specialised capabilities can be developed through project delivery, coaching and structured learning. Initial Project Context The initial project is a reusable questionnaire and evidence collection capability. The platform is intended to support common questionnaire patterns such as template configuration, question management, response capture, evidence upload, workflow status tracking and audit trail generation. The target design may involve workflow orchestration, metadata modelling, SQL-based evidence and audit persistence, and secure API-driven integration with platform and consuming application services. Technologies such as Apache Airflow, graph databases and Azure services may be used where appropriate. Key Responsibilities Data Engineering and Delivery Design, build and maintain data pipelines, data services and integration components for Digital Factory solutions. Develop maintainable Python components for data processing, automation, integration and operational workflows. Translate business and platform requirements into reliable technical designs, data models and engineering deliverables. Support reusable platform capabilities that can be adopted across multiple consulting digital solutions. SQL, Data Modelling and Auditability Design SQL schemas for operational data, evidence metadata, workflow status history, notification records and audit events. Support auditability, traceability, status history and evidence metadata patterns. Design secure references to documents or object storage, including metadata, versioning, ownership and retention attributes. Support reporting, dashboarding and export needs through reliable data structures. Workflow, Integration and Operations Support orchestration patterns for scheduling, reminders, escalations, reporting and controlled reruns. Build integration components for API-driven, asynchronous or event-driven workflows. Implement logging, monitoring, diagnostics, automated tests and data quality checks. Document data models, workflow behaviour, technical decisions, runbooks and support procedures. Security, Privacy and Governance Apply secure data handling practices, including access control, encryption, secrets management and least privilege principles. Support expectations around auditability, lineage, retention and evidence generation. Work with project managers, architects, backend engineers, QA, security, privacy and business stakeholders. Required Qualifications and Experience Relevant professional experience as a Data Engineer, Platform Engineer, Data-focused Software Engineer or similar technical role. Hands-on experience with Python for pipeline, automation, integration or service development. Strong SQL skills, including schema design, operational data modelling and query optimisation. Experience designing reliable data pipelines, data services or integration workflows. Experience with source control, CI/CD, automated testing, code reviews and maintainable production code. Understanding of data security, access control, auditability, observability and operational monitoring. Strong communication skills and ability to work with technical and non-technical stakeholders. Ability to work in a structured project environment with clear documentation and delivery discipline. Preferred Qualifications and Experience Experience with Apache Airflow or equivalent orchestration tools. Experience with graph data modelling or graph technologies such as Neo4j, Cypher, Cosmos DB Gremlin or equivalent tools. Experience with Azure services such as Azure SQL, Cosmos DB, Azure Storage, Azure Functions, Service Bus, Event Hubs, Key Vault or Application Gateway. Experience with API-driven integration, asynchronous processing or event-driven workflows. Experience in financial services, regulatory, audit, compliance, workflow, questionnaire or client-facing platform environments. Familiarity with Docker, Terraform, Bicep, PySpark or infrastructure-as-code practices. Technical Skill Set Must-Have Python SQL and relational database experience Data pipeline design and implementation Data modelling for operational, reporting or audit use cases API or integration-oriented engineering experience Source control, CI/CD, testing and code review practices Observability, logging, monitoring and troubleshooting Production-grade engineering discipline and clear documentation Nice-to-Have Apache Airflow Graph data modelling and graph query experience Neo4j, Cypher, Cosmos DB Gremlin or equivalent technologies Azure SQL, Cosmos DB, Azure Storage, Azure Functions, Service Bus or Event Hubs Async or event-driven integration patterns Docker, Terraform, Bicep or PySpark Financial services, compliance, workflow or questionnaire platform experience Expected Profile The ideal candidate is a hands-on data engineer with strong engineering judgement, delivery discipline and a practical mindset. They should be able to work independently, clarify requirements, identify edge cases, collaborate with architects and project teams, and deliver reliable, well-documented engineering outputs. Prior experience with Apache Airflow, graph databases or Azure services is an advantage, but not a prerequisite. The role is suitable for a candidate with strong core data engineering foundations and the motivation to develop specialised platform skills over time. What we offer you At EY, we’ll develop you with future-focused skills and equip you with world-class experiences. We’ll empower you in a flexible environment, and fuel you and your extraordinary talents in a diverse and inclusive culture of globally connected teams. Learn more . Are you ready to shape your future with confidence? Apply today. To help create the best experience during the recruitment process, please describe any disability-related adjustments or accommodations you may need. EY | Building a better working world EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets. Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow. EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories. Our offer of employment is contingent upon the successful completion of a background check and pre-screening requirements. The candidate acknowledges that all information provided must be accurate.