Data Platform Engineer

This is a fully remote position, open to applicants in Slovakia.

📋 Description

• Design and deploy cloud-based data platform infrastructure utilizing Infrastructure as Code (Terraform), emphasizing scalability, security, reliability, and cost-effectiveness.

• Develop and sustain CI/CD pipelines to automate data engineering workflows, data pipeline deployments, and infrastructure provisioning, facilitating quicker deployment cycles and reducing errors.

• Implement and manage observability solutions that integrate monitoring, logging, and metrics to ensure the reliability of the platform, visibility of performance, and rapid incident response.

• Work in close collaboration with data engineers and cross-functional teams to create and implement data pipelines, data models, and platform features that fulfill performance and business requirements.

• Follow best practices for high availability, disaster recovery, security, and cost optimization, while documenting infrastructure patterns, data architecture choices, and operational procedures.

⛳️ Requirements

• 3-5 years of practical experience in Data Engineering, Platform Engineering, or DataOps positions.

• Demonstrated success in designing and implementing dependable, scalable data platforms and data infrastructure — not merely supporting but owning the complete delivery process.

• Required hands-on experience with contemporary data engineering tools such as dbt, Apache Airflow, or Apache Kafka.

• Proficiency with Infrastructure as Code (Terraform) and cloud architecture patterns on AWS or GCP is essential.

• Extensive experience with AWS or GCP, encompassing data storage and processing services (e.g., BigQuery, Snowflake, S3, Redshift).

• Practical knowledge of Kubernetes and containerized workloads for orchestrating data platform services.

• Experience in implementing observability stacks for monitoring, logging, metrics, and alerting on data platforms.

• Strong programming abilities in Python, SQL, and Bash for building data pipelines, automating workflows, and executing data processing tasks.

• Excellent problem-solving capabilities and the ability to work efficiently in a collaborative, fully remote setting.

• A strong desire to enhance expertise in data architecture, data modeling, and MLOps capabilities.

• Experience with real-time data processing (e.g., Kafka, Spark Streaming) and familiarity with both SQL and NoSQL data storage solutions is a plus.

🏝️ Benefits

• Professional development opportunities

• Flexible working hours