Data Engineer

Area within Group Digital:
Data & Analytics

Background

The Marketing Activation area (Performance, Strategy, Acquisition, Organic Search, and CRM) is undergoing a major digital transformation. Our goal is to build a unified Marketing Data Foundation that will serve as the single source of truth for the entire area. This is occurring alongside an Adobe Experience Platform (AEP) implementation and a structural shift toward Data Mesh principles.

We face a bridgeable gap between our current technical debt and our future-state ambitions. We need a senior-level consultant to act as a "right hand" to leadership—someone who can navigate the complexities of a changing organization, build production‑grade pipelines, and elevate the technical maturity of our Data & ML (D&ML) team.

• Building the Marketing Data Foundation:
  Collaborating with Software Engineering, Architects, and Product owners to design and implement a robust, scalable "source of truth" for marketing data.
• Pipeline Engineering (CRM):
  Performing hands‑on development of data pipelines, specifically within the CRM domain, to automate workflows and reduce the burden on data analysts.
• Advanced dbt & Semantic Layer:
  Scaling our dbt setup, focusing on project organization, governance, and the implementation of a dbt Semantic Layer to prepare for ML and Agentic AI.
• Team Education & Mentorship:
  Upskilling the D&ML team on "how we should work," including Git best practices, code reviews, and engineering excellence.
• Data Productization:
  Defining and registering work as "Data Products" in accordance with Data Mesh principles and architecture standards.

• GCP & Big Query Expert:
  Deep technical proficiency in Google Cloud Platform and Big Query optimization for large‑scale datasets.
• Hands‑on Engineering:
  Proven experience building and maintaining automated ETL/ELT pipelines.
• Git Mastery:
  Experience with version control, branching strategies, and CI/CD.
• Expert‑level dbt:
  Professional experience in modeling, particularly with dbt projects at scale.
• Mentorship
  
  Skills:
  
  The ability to coach junior/mid‑level engineers and influence "ways of working" through documentation and pair programming.
• Data Mesh & Architecture: A strong understanding of Data Mesh and how to navigate the "Technical Lead" role.
• Stakeholder Management:
  Experience working across departments (Software Engineering, Architects, Product) to align on a shared data vision.
• AI/ML/Agentic Readiness:
  Experience structuring data for ML, LLMs and automated agents.
• AEP Awareness:
  Familiarity with Adobe Experience Platform to assist in the transition phase.

What 3 things from the box above are most important?

• Technical Execution & dbt Expertise (GCP/Big Query/dbt): A hands‑on engineer who can build production‑grade CRM pipelines and resolve technical debt. Must possess expert‑level dbt skills to implement a sophisticated Semantic Layer and ensure the data architecture is ML and Agentic‑ready.
• Mentorship & Upskilling:
  The ability to actively educate and lead the Data & ML (D&ML) team on "what good looks like." This includes establishing and teaching high standards for Git flow, code reviews, and general engineering rigor.
• Data Product & Foundation Mindset: A consultant who can move the team toward a Data Mesh state by structuring work as reusable, well‑documented data products. They will be a key player in co‑creating the Marketing Data Foundation (Source of Truth) alongside architects and software engineering.