Belmont Lavan Ltd

    Data Engineer- Dremio Spark Airflow - 100 Percent Remote - 6 months Contract

    Belmont Lavan Ltd
    Posted 11/11/2025Mid Level
    Contract
    Technology
    Data Engineering
    ETL
    Apache Spark
    Airflow
    Kubernetes

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    Job Description

    NOTE: This position is 100% remote however you have to be based in Finland, cannot hire people outside of Finland. Position: Data Engineers x 5 (Dremio Spark Airflow) - 100% Remote - 6 months Contract. About the Role We’re expanding our telecom data platform team and are looking for talented Data Engineers to help design, build, and optimise large-scale data pipelines supporting analytics and automation across our telecom systems. You’ll play a key role in developing the next generation of our data integration and analytics framework, using technologies such as Apache Spark, Airflow, Kubernetes, and Dremio. This role is ideal for someone who enjoys working with distributed systems, complex data flows, and cutting-edge infrastructure. What You’ll Do Design, develop, and maintain scalable data pipelines and ETL processes. Integrate data from various telecom domains including CRM, billing, and network systems. Build efficient data transformation and orchestration workflows using Spark and Airflow. Manage deployments in Kubernetes on Linux-based environments (RHEL or equivalent). Optimise data accessibility and analytics performance through Dremio and related tools. Automate scheduling and monitoring to ensure stability and reliability. Troubleshoot data issues, conduct performance tuning, and maintain platform documentation. What You’ll Bring Proven experience in data engineering and ETL workflow design. Strong working knowledge of Apache Spark, Airflow, and Kubernetes. Solid programming skills in Python, Java, and SQL. Hands-on experience with Red Hat Enterprise Linux (RHEL) or similar environments. Strong analytical, problem-solving, and communication skills. Nice to Have Experience working with telecom data (customer, billing, or network). Familiarity with data lake, mesh, or real-time streaming architectures. Exposure to cloud platforms such as AWS, Azure, or GCP. Understanding of CI/CD automation and data governance frameworks.

    Language:

    Fluent English

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