Data Engineering Lead / Evangelist

Responsibilities

• Define and drive data engineering standards & best practices: Establish and maintain robust engineering guardrails that are adopted by teams across business units
• Community leadership & evangelism: Organize and facilitate Guild events such as meetups, tech talks, office hours, and hackathons to build an active, engaged data engineering community. Serve as a visible champion for shared success, encouraging collaboration and knowledge-sharing across teams
• Promote reusable patterns & platform adoption: Evangelize reusable engineering patterns, platform capabilities, and shared assets
• Develop and curate learning programs: Own and continuously improve NN's Data Engineering learning track (part of our internal Data Literacy Program). Ensure the curriculum remains up-to-date, relevant, including creating engaging exercises and assessments
• Training & onboarding enablement: Deliver training workshops, and onboarding programs for new and experienced data engineers. Act as a coach and mentor, fostering an enablement culture where engineers across all business units can advance their skills
• Document and communicate effectively: Develop clear documentation, guidelines, FAQs, and playbooks, and ensure these knowledge assets are well-structured, up-to-date, and easily accessible
• Cross-functional collaboration: Represent Data Engineering in cross-domain forums and communities of practice (e.g. with other guilds Data Science, Architecture, or Engineering)
• Influence without direct authority: Work closely with data engineering teams in different business units and the central Data Platform team to align on best practices and standards
• What we offer you

Requirements

• You have a bachelor's or master's degree and corresponding thinking level
• You have 5+ years of experience in data engineering and/or data platform development in a large-scale enterprise environment
• You have strong hands-on experience with lakehouse/data platform technologies (Databricks preferred) and modern data engineering practices
• You have a solid understanding of lakehouse architectures, data modeling (including medallion patterns), ingestion and transformation patterns, and data quality concepts
• You are comfortable working in an agile environment and can drive initiatives through influence rather than formal authority
• You communicate clearly, facilitate groups confidently, and can adapt your message to both technical and non-technical stakeho

Benefits

Additional Information

NN Digital Hub is a subsidiary company of Nationale Nederlanden Group located in Madrid, Spain. Our main objective is to create, deliver and maintain insurance product management solutions, as service propositions to enable any agent (linked or direct), machine or company to create and sell insurance products in Europe in an automated way and real time. We deliver IT services and solutions for the different international Business Units from Nationale Nederlanden Group. For the Data & AI department at NN Group IT we are seeking an experienced Data Engineering Lead / Evangelist who will help raise the data engineering capability across the organization. This role is a core team member of the Data Engineering Guild driving standards, best practices, and community enablement and acting as Subject Matter Expert (SME) for the DLP Data Engineering learning track. The NN Data Engineering Guild consists of 270+ data engineers and data platform engineers spread across 10+ Business units/ countries. What you are going to do In this role you will be a technical leader, coach, and community builder for NN's Data Engineering Guild - our global community of data engineers (270+ members). Your will raise the data engineering maturity across a large, federated enterprise by defining best practices, fostering an engaged engineering community, and driving adoption of common tools and platforms. By combining deep hands-on expertise with exceptional communication and coaching skills, you will help diverse teams deliver trusted, scalable, and well-governed data products more efficiently.

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