Digital Factory - Data Engineer - Assistant Director (Luxembourg, LU, L-1855)

Digital Factory - Data Engineer – Assistant Director

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We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go.​

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The opportunity

TheData Engineerprovides strategic, technical, and operational leadership for the design, delivery, and operation of enterprise‑grade data platforms that underpin EY’s digital products, analytics, and data‑driven transformation initiatives.

This role goes beyond hands‑on delivery toowning data engineering outcomes end‑to‑end, shaping platform strategy, setting standards, and leading teams to deliverscalable, reliable, secure, and compliant data solutionsin highly regulated environments.

You will act as atrusted technical leader and advisorto Product, Architecture, Analytics, Data Science, and senior stakeholders, ensuring data platforms are fit for purpose, future‑proof, and aligned with EY’s broader technology and risk strategy.

Key Responsibilities

Data Platform Strategy & Technical Leadership

• Define and drive thedata engineering strategy and roadmapaligned with product, analytics, and enterprise architecture objectives.
• Own thetechnical vision and target architecturefor data platforms (lakehouse, warehouse, streaming, and event‑driven patterns).
• Make and governarchitecture decisions, balancing scalability, cost, performance, security, and regulatory requirements.
• Act as thedesign authorityfor complex data pipelines and platforms, reviewing and approving solution designs.

Advanced Data Platform & Pipeline Engineering

• Oversee the design and implementation ofenterprise‑scale batch and streaming data pipelines, using Python and modern data frameworks.
• Ensure robust ingestion, transformation, and serving layers across internal and external data sources (APIs, databases, files, events).
• Drive the development ofreusable data engineering frameworks, accelerators, and reference implementations.
• Establish and enforcedata contracts, schemas, versioning strategies, and documentation standardsacross teams.

Data Quality, Reliability & Operational Excellence

• Set and owndata quality, reliability, and observability standardsacross all managed data products.
• Define and governSLAs/SLOsfor critical data assets, ensuring business‑critical use cases are protected.
• Leadmajor incident management and root‑cause analysis, ensuring durable, systemic fixes rather than tactical workarounds.
• Embedtesting strategies(unit, integration, data quality, regression) as non‑negotiable engineering standards.

Performance, Scalability & Cost Governance

• Ensure platforms and pipelines are designed forscale, resilience, and fault tolerance(idempotency, retries, checkpointing, backpressure).
• Drive continuous optimization ofcompute usage, storage layouts, query performance, and costs.
• Make data‑driven trade‑off decisions between performance, cost, complexity, and maintainability.

People Leadership & Team Enablement

• Providetechnical leadership, mentoring, and coachingto Data Engineers across multiple teams or initiatives.
• Set clear expectations forengineering quality, delivery discipline, and professional development.
• Support capacity planning, skill development, and succession planning within the data engineering capability.
• Act as an escalation point for complex technical and delivery challenges.

Cross‑Functional & Stakeholder Collaboration

• Partner closely withProduct, Architecture, Backend Engineering, Analytics, and Data Science leadsto translate strategic objectives into executable data solutions.
• Engage senior stakeholders toexplain technical trade‑offs, risks, and investment needs in clear, business‑aligned language.
• Ensure data platform decisions support downstream consumption patterns (APIs, analytics, ML, operational use cases).

Security, Privacy, Risk & Compliance Ownership

• Own the implementation and governance ofsecure‑by‑design data engineering practices(access controls, encryption, secrets management).
• Ensure platforms comply withenterprise, regulatory, and privacy obligations(data classification, lineage, retention, auditability).
• Act as a key contributor toaudits, risk assessments, and data governance forums, representing the data engineering domain.

Operations, Governance & Continuous Improvement

• Hold accountability forproduction data platforms, ensuring operational stability, availability, and ongoing improvement.
• Reduce operational risk and toil throughautomation, standardization, and platform‑level capabilities.
• Define, maintain, and evolvedata engineering standards, guardrails, and best practicesacross the Digital Factory.
• Contribute to broadertechnology governance and platform strategyat program or portfolio level.

Qualifications Required

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Software Engineering, or a related discipline.
• Extensive professional experience indata engineering, including leadership or solution‑ownership responsibilities.
• Deep expertise inPythonfor large‑scale, production‑grade data engineering.
• Strong command ofSQL and data modeling(dimensional, normalized, lakehouse, or hybrid patterns).
• Proven experience designing and operatingenterprise data platforms(data lakes, warehouses, analytical stores).
• Solid experience withCI/CD, automated testing, code quality, and engineering governancefor data workloads.
• Strong understanding ofcloud platforms and data services(Azure strongly preferred).
• Demonstrated ownership ofdata security, access control, observability, and operational reliability.

Preferred

• Strong hands‑on and design experience withmodern data ecosystems(e.g., Spark, Databricks, Airflow, dbt, Azure Data Factory).
• Experience withstreaming and event‑driven architectures(Kafka, Azure Event Hubs, Service Bus).
• Prior ownership oflakehouse or enterprise data warehouse strategies.
• Experience operating infinancial services or other regulated environments.
• Familiarity withinfrastructure‑as‑code(Terraform, Bicep) and platform automation.
• Proven success working acrossdistributed, cross‑functional, and global teams.

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.

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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.