Data Platform Engineer

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

📋 Description

• Design and develop cloud-based data platform infrastructure utilizing Infrastructure as Code (Terraform), with a significant emphasis on scalability, security, dependability, and cost-effectiveness.

• Construct and oversee CI/CD pipelines that automate data engineering workflows, data pipeline deployments, and infrastructure provisioning, ensuring accelerated deployment cycles and reduced errors.

• Implement and manage observability solutions by integrating monitoring, logging, and metrics to guarantee platform reliability, performance visibility, and rapid incident response.

• Work closely with data engineers and cross-functional teams to design and execute data pipelines, data models, and platform capabilities that fulfill performance and business requirements.

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

⛳️ Requirements

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

• Demonstrated success in designing and implementing reliable, scalable data platforms and data infrastructure — not merely supporting, but taking full ownership of end-to-end delivery.

• Hands-on experience with contemporary data engineering tools such as dbt, Apache Airflow, or Apache Kafka is essential.

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

• Extensive experience with AWS or GCP, including 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 within data platforms.

• Strong programming capabilities in Python, SQL, and Bash for building data pipelines, automating workflows, and conducting data processing.

• Excellent problem-solving abilities and the capacity to work effectively in a collaborative, fully remote setting.

• A keen interest in enhancing 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

• Health insurance

• Retirement plans

• Paid time off

• Flexible work arrangements

• Professional development