Lead Data Engineer (#5552)

N-iX is a global software development service company that helps businesses across the globe create next-generation software products. Founded in 2002, we unite 2,400+ tech-savvy professionals across 40+ countries, working on impactful projects for industry leaders and Fortune 500 companies. Our expertise spans cloud, data, AI/ML, embedded software, IoT, and more, driving digital transformation across finance, manufacturing, telecom, healthcare, and other industries. Join N-iX and become part of a team where your ideas make a real impact.

We are looking for a Lead Data Engineer to join our team and contribute to one of Europe's leading online automotive marketplaces, serving 30+ million monthly users across 18 countries.

As a Lead Data Engineer, you will lead a team of data engineers and analysts, partnering closely with business stakeholders and senior analysts to translate marketplace analytics needs — from web & app tracking to marketing performance — into robust data models and pipelines that power the company's key business decisions.

This role sits closer to the business than a typical data engineering position. The Data Platform Engineering team owns the underlying platform and infrastructure, so you'll focus on understanding analytics use cases, shaping data models, and enabling analysts and stakeholders to answer their most important business questions.

Responsibilities:

• Lead and mentor a team of data engineers, providing technical direction as well as people leadership.
• Partner with business stakeholders and senior analysts to gather requirements and translate business KPIs and OKRs into data models and pipelines.
• Design, build, and maintain scalable data models that support marketplace analytics, including web/app tracking and marketing use cases.
• Write and optimize complex SQL to transform and model data for analytics consumption.
• Build and troubleshoot code-based data pipelines using Python, Spark, and Airflow, jumping in hands-on when needed while guiding the wider engineering team's pipeline work.
• Champion data quality by implementing and maintaining data testing practices across the team's pipelines.
• Evaluate and apply semantic layer concepts to make data more accessible and consistent for business users.
• Explore and adopt AI tooling provided by the company to improve team productivity and data workflows.
• Act as a bridge between the Data Platform Engineering team (who own infrastructure) and the business, ensuring analytics needs are well understood and delivered.
• Deliver on product requirements on time while keeping tech debt and redundancy to a minimum, following best practices and standards.
• Keep yourself and stakeholders aligned with clear, ongoing visibility into project status, surfacing risks early along with a plan and timeline for resolving them.

Requirements:

• Proven experience leading a data engineering team — this role requires genuine people leadership, not just senior-level technical skills.
• Strong ownership mindset, with a track record of delivering reliably on time while minimizing tech debt and redundancy and adhering to best practices and standards.
• 5+ years of hands-on experience in Data Engineering.
• Hands-on experience building data pipelines with Python and Spark — the project's pipelines are code-based (not SQL/dbt-based) — with the ability to jump in and contribute directly when needed, as well as a deep enough understanding to follow and guide what other engineers on the team are building.
• Hands-on experience with Airflow for orchestrating data pipelines.
• Proficiency with Git for version control.
• Strong SQL skills, with the ability to write and optimize complex queries (experience with any SQL dialect is fine — prior exposure to Athena specifically is not required).
• Solid data modeling skills, with experience designing models that serve analytics and business intelligence use cases.
• Excellent communication skills, with the ability to engage business stakeholders and senior analysts to gather requirements and understand KPIs/OKRs.
• Familiarity with data testing practices and tools.
• Familiarity with AI tooling for data engineering workflows.
• English proficiency at B2/C1 level or higher.

Nice to have:

• Experience with marketplace analytics, including web & app tracking and a good understanding of marketing/business metrics.
• Previous experience working with a semantic layer.
• Experience with AWS — the project runs on an AWS data lake, so this is an advantage, though equivalent experience with other cloud providers is also fine given the transferable concepts.