Data Platform Engineer
New
P
PaymentologyFintech
Location: Norway; Secondary Locations: Bosnia & Herzegovina, Croatia, Lithuania, Spain, Cyprus, Moldova, Greece, Portugal, Ireland, Romania, Latvia, Serbia, SlovakiaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 3-5 years
- Required Skills
- AWSPythonSQLApache AirflowBashGCPKubernetesApache KafkaTerraformdbt
Requirements
- 3-5 years of hands-on experience in Data Engineering, Platform Engineering, or DataOps roles.
- Proven track record in designing and implementing reliable, scalable data platforms and data infrastructure.
- Hands-on experience with modern data engineering tools such as dbt, Apache Airflow or Apache Kafka.
- Hands-on proficiency with Infrastructure as Code (Terraform) and cloud architecture patterns on AWS or GCP.
- Deep experience with AWS or GCP, including data storage and processing services (e.g., BigQuery, Snowflake, S3, Redshift).
- Practical experience with Kubernetes and containerised workloads for orchestrating data platform services.
- Experience implementing observability stacks for data platform monitoring, logging, metrics, and alerting.
- Strong programming skills in Python, SQL, and Bash.
- Excellent problem-solving skills and the ability to work effectively in a collaborative, fully remote environment.
Responsibilities
- Design and implement cloud-based data platform infrastructure using Infrastructure as Code (Terraform), with a strong focus on scalability, security, reliability, and cost-efficiency.
- Build and maintain CI/CD pipelines that automate data engineering workflows, data pipeline deployments, and infrastructure provisioning, ensuring faster deployment cycles and minimizing errors.
- Implement and operate observability solutions — integrating monitoring, logging, and metrics to ensure platform reliability, performance visibility, and fast incident response.
- Collaborate closely with data engineers and cross-functional teams to design and implement data pipelines, data models, and platform capabilities that meet performance and business requirements.
- Apply best practices for high availability, disaster recovery, security and cost optimization, while documenting infrastructure patterns, data architecture decisions, and operational procedures.
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