Senior/Lead Data Engineer

About this role

N-iX is looking for Senior/Lead Data Engineer to join the team About the client Our client is a leading European online car marketplace, connecting car buyers and sellers across the continent. The platform serves over 30 million monthly users and operates across 18 countries, making it one of the largest and most trusted digital marketplaces for vehicles in Europe. About the role We are looking for a Data / Analytics Engineer to support the client team.

The role sits close to the data analysts and business stakeholders, with a primary focus on maintaining and extending the data pipelines and data models that power analytics and reporting. The core stack includes SQL, Airflow, Athena, AWS Data Lake, and AI-assisted engineering tools such as Codex. The client is currently migrating away from DBT and Snowflake to the clients data platform. The destination cloud and data platform already exists and is operational, and the migration itself will be handled by a different team — so this is not an infrastructure or platform engineering role.

Instead, we need an engineer who can understand analytics use cases, translate business requirements into robust data solutions, and work independently to get those solutions into production. Beyond the technical skills, we are looking for someone with a strong sense of ownership and engineering judgment: a person who does not simply execute a ticket or accept a blocker at face value, but actively clarifies unclear requirements, investigates problems, proposes alternatives when the obvious approach does not work, and involves the right people when decisions are needed.

Responsibilities: Maintain, troubleshoot, and improve existing data pipelines, and build new pipelines and datasets based on analytics and business requirements. Design and maintain analytical data models, including fact and dimension models. Ingest and integrate data from different source systems into the AWS Data Lake. Build, deploy, validate, monitor, and, when necessary, backfill data pipelines. Partner with data analysts to understand their requirements and provide reliable, easy-to-use datasets.

Clarify requirements with business stakeholders rather than implementing ambiguous requests as-is. Ensure data quality through appropriate validation and testing, investigating data and pipeline issues and taking ownership of finding pragmatic solutions end to end. Use AI-assisted development tooling effectively while critically reviewing and validating generated solutions. Requirements: Hands-on data engineering experience building and operating production data pipelines, ideally within analytics-driven environments.