Big Data Development Engineer

Vhomeos — Netherlands · Posted ~3 hours ago

Mid Full-time

Skills

Python Java SQL ETL Data pipelines Data warehouse modeling Hadoop Spark Kafka

🔓 Log in to save this job, tailor your resume & track your apply process — 7 days free, no card needed.

Log in to add to target list

Summary ✨ AI‑Generated

A data engineering role focused on designing ETL workflows, data services, reporting solutions, and scalable big data processing systems.

Highlights

Full-time opportunity working on large-scale data platforms, analytics solutions, and cross-functional international projects.

Description

Big Data Development Engineer Openings: 2 Full-Time Positions Location: Netherlands I. Responsibilities Develop, schedule, and maintain offline/real-time ETL tasks to ensure the stability and timeliness of data pipelines;Develop and optimize data reports, data service APIs, and data products related to user tags and user profiling;Collaborate with overseas business stakeholders and technical teams to gather requirements and co-develop solutions, participating in communication and delivery for cross-country and cross-regional projects.II. Qualifications Bachelor’s degree or above in Computer Science or a related field, with at least 3 years of experience in data development using Python and Java;Proficient in SQL, with solid experience in big data warehouse modeling, ETL development, and data reporting development, and able to independently produce technical design and development documentation;Familiar with the Hadoop big data ecosystem, including HDFS, Hive, Spark, Flink, Kafka, etc., with hands-on experience in performance tuning;Good English communication skills, capable of independently participating in international meetings, email communication, and English document reading/writing;Strong communication skills, proactive working attitude, strong sense of responsibility, and good teamwork and stress management abilities.III. Preferred Qualifications Familiarity with AWS big data ecosystem components, such as EMR, S3, Glue, Athena, Redshift, and other cloud-based data solutions;Experience with Airflow scheduling platforms, Power BI development, and implementation of user tagging and user profiling projects;Experience in data governance and data quality framework development.