Data Engineer developing and enhancing data pipelines and models at ERNI Schweiz. Required skills include SQL and Python with opportunities for remote work in Europe.
About the role
- DataOps Engineer at Eeze focusing on data pipeline stability across multiple products. Collaborating with IT teams to maintain quality, observability, and operational efficiency.
Responsibilities
- Ensure the reliable and timely execution of daily data pipelines and scheduled workflows.
- Operate and maintain internal data services, including ingestion layers, OLAP/lake storage, materialised views, and task dependencies.
- Contribute to CI/CD workflows for data pipelines and participate in deployments, version management, and change control.
- Monitor orchestration systems (e.g., Airflow), troubleshoot pipeline failures, delays, and anomalies, and drive continuous performance improvements.
- Implement and maintain data quality checks, anomaly detection, schema validation, and audit processes.
- Collaborate with Data Engineers on table lifecycle management, storage optimisation, partitioning strategies, and schema evolution.
- Work with IT Infrastructure and IT Operations teams to improve platform observability, including logging, metrics, and alerting.
- Develop and maintain SOPs, platform standards, best practices, and troubleshooting documentation.
- Provide operational support to internal users (DE/DA/DS/Ops) for issues such as query performance, missing data, or inconsistent KPIs.
Requirements
- 2+ years of experience in Data Ops, Data Engineering, BI Engineering, or a similar operational data role.
- Experience with CI/CD workflows, Docker, Kubernetes, or other DevOps-related practices.
- Hands-on experience with workflow orchestration tools such as Airflow (or equivalent).
- Familiarity with mainstream data engineering technologies such as Kafka, Spark, Flink, Delta Lake, Iceberg, Hudi, ClickHouse, or Doris.
- Good understanding of data warehousing concepts, including partitioning, schema evolution, table lifecycle management, and OLAP vs. data lake architectures.
- Strong SQL skills and familiarity with Python for scripting, automation, or validation.
- Strong debugging and problem-solving skills, especially for data anomalies and pipeline failures.
- Comfortable working cross-functionally with DE/Infra/Ops/DA/DS teams in a fast-paced environment.
- Mandarin proficiency is preferred
Benefits
- Flexible working hours
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