Data Platform Engineer

P
PaymentologyFintech Payments
Norway, Romania, Ireland, Moldova, Lithuania, Slovakia, South Africa, Bosnia & Herzegovina, Serbia, Portugal, Cyprus, Croatia, Greece, Spain, Estonia, KenyaFull-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 containerized 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 to build data pipelines and automate workflows.
  • Excellent problem-solving skills and the ability to work effectively in a collaborative, fully remote environment.
  • Inclination to deepen expertise in data architecture, data modeling, and MLOps capabilities.

Responsibilities

  • Design and implement cloud-based data platform infrastructure using Infrastructure as Code (Terraform), with a 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.
  • Implement and operate observability solutions, integrating monitoring, logging, and metrics for performance visibility and incident response.
  • Collaborate with data engineers and cross-functional teams to design and implement data pipelines, data models, and platform capabilities.
  • Apply best practices for high availability, disaster recovery, security, and cost optimization, while documenting architecture and procedures.
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