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Salary
≈ $145k – $325k per year (Estimated)
Location
In office (Luxembourg City)
Seniority
Architect

Confirmed on the employer's own hiring board on Oct 19, 2026. First seen by Alion on Oct 18, 2026.

Overview
Company
Impact
Profile match

EY

Headquartered in London, United Kingdom, EY (Ernst & Young) is a multinational professional services network and one of the "Big Four" accounting firms. The organization provides financial assurance and auditing, tax advisory, management and technology consulting, and strategy and transactions advisory through its EY-Parthenon arm. Operating across more than 150 countries, it delivers risk management, digital transformation, data analytics, and regulatory compliance solutions to multinational corporations, financial institutions, and government entities worldwide.
Backed by ROPEA

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.

The Data Engineering Manager - Assistant Director supports multiple teams across the Digital Factory in a matrix setup, with a focus on data engineering practices and solutions.

The role is responsible for guiding teams on architecture, technology standards and engineering practices, while contributing hands-on to the design and implementation of data solutions using platforms such as Microsoft Fabric and Databricks.

The position works closely with delivery, architecture, and business teams to ensure consistency, quality and reuse across data initiatives.

Key Responsibilities

Engineering Mentorship & Capability Building

  • Mentor and coach engineers across multiple teams on data engineering practices, architecture, and delivery quality
  • Support technical decision-making and problem solving in complex data initiatives
  • Raise overall engineering maturity on:
  • Data pipelines (ETL / ELT)
  • Lakehouse architectures
  • Spark / Databricks / Fabric usage
  • Promote a hands-on engineering mindset and craftsmanship culture

Architectural Alignment & Guardrails

  • Ensure all data solutions align with Digital Factory architecture principles and guardrails
  • Guide teams on:
    • Data architecture patterns (lakehouse, data mesh, etc.)
    • Platform usage (Fabric, Databricks, Azure services)
  • Act as bridge between Architecture and Delivery teams
  • Challenge and validate technical design decisions when needed

Technology Standards & Best Practices

  • Define and enforce engineering standards for data platforms, including:
    • Data modeling approaches (dimensional / analytical)
    • Pipeline design and orchestration
    • CI/CD and DataOps practices
  • Standardize reusable components, templates, and practices
  • Ensure consistency across teams working on different projects

Hands-on Contribution

  • Actively contribute to design and implementation of data solutions, especially in complex or critical topics
  • Support teams in:
    • Building scalable data pipelines (Fabric, Databricks, Spark)
    • Optimizing performance and cost
    • Troubleshooting complex technical issues
  • Lead by example with a strong hands-on engineering approach

Cross-Team Coordination

  • Work across multiple project teams to ensure consistency and reuse
  • Collaborate with:
    • Architecture
    • Project / Business leads
    • Data / AI engineers
  • Facilitate knowledge sharing and alignment across initiatives

Platform & Reuse

  • Encourage reuse via shared libraries, components, and templates; maintain documentation for architecture decisions and reusable assets.

Qualifications

Required

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
  • Fluency in English. Any additional language(s) are highly appreciated.
  • Demonstrated experience leading software engineering teams as an Engineering Manager or Senior Tech Lead, delivering production systems in data domains.
  • Strong experience in data engineering and platform delivery
  • Proven experience as:
    • Senior Data Engineer / Lead
    • OR Engineering Manager in data context
  • Hands-on expertise in:
    • Microsoft Fabric or Azure Data Platform
    • Databricks (Spark, Delta Lake)
    • Data pipelines, lakehouse architectures
  • Strong understanding of data architecture, governance, and platform design
  • Ability to influence teams in a matrix organization (without direct authority)

Preferred

  • Experience in consulting / Digital Factory / multi-project environments
  • Exposure to data governance frameworks and enterprise architecture
  • Familiarity with AI/analytics data use cases

Tools & Technologies

  • Microsoft Fabric (OneLake, Data Factory, Notebooks)
  • Azure Databricks (Spark, Delta Lake)
  • Azure Data Services (ADF, Synapse, SQL)
  • Python, SQL
  • CI/CD (Azure DevOps)
  • Data governance & observability tooling

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.

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