Data Engineer: Own & Scale End-to-End Data Pipelines

Luxurynsight group; Paris; On-site; EUR 90,000 - 130,000; Full time

Job summary

Heuritech is seeking an experienced Data Engineer to design, build, and scale the data infrastructure that turns raw crawled data into trusted, usable datasets for the entire company. You will own end-to-end data pipelines, from ingestion to modeling, ensuring robustness, observability, and high throughput.

You will work with modern stack including Airflow, dbt, Snowflake, Celery, Kubernetes, AWS, and Datadog, and collaborate with Data Science, Product, and Engineering teams to enable

Qualifications

• 3–5+ years in Data Engineering or Software Engineering with a strong data focus.
• Own the full lifecycle of a data pipeline from ingestion to orchestration to modeling.
• Experience with large volumes of data and scalability; robust, observable systems.
• Proficient in Python and SQL; comfortable with Linux/macOS environments.

Responsibilities

• Designing and maintaining Airflow DAGs and orchestration logic.
• Building and evolving dbt models and transformations in the Snowflake data warehouse.
• Improving reliability, monitoring, and data quality of pipelines.
• Working on crawling stack and its infrastructure feeding the pipeline.
• Contributing to internal tools to make data more accessible across teams.
• Exploring and prototyping new tools or architectures to improve data orchestration.

Skills

Python

SQL

Data modeling

Data pipelines

CI/CD

Git

Linux/macOS

Tools

dbt

Airflow

Snowflake

Celery

Kubernetes

Docker

AWS

Datadog

OpenTelemetry

Job description

Heuritech is seeking an experienced Data Engineer to design, build, and scale the data infrastructure that turns raw crawled data into trusted, usable datasets for the entire company. You will own end-to-end data pipelines, from ingestion to modeling, ensuring robustness, observability, and high throughput.

You will work with modern stack including Airflow, dbt, Snowflake, Celery, Kubernetes, AWS, and Datadog, and collaborate with Data Science, Product, and Engineering teams to enable