Data Platform Engineer

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

📋 Description

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

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

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

• Work collaboratively with data engineers and cross-functional teams to design and execute data pipelines, data models, and platform functionalities that align with performance and business objectives.

• Employ 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 roles.

• Demonstrated success in designing and implementing dependable, scalable data platforms and data infrastructure — taking ownership of end-to-end delivery rather than just support.

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

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

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

• Practical experience with Kubernetes and containerized workloads for orchestrating data platform services.

• Background 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.

• Exceptional problem-solving abilities and the capacity to thrive in a collaborative, fully remote environment.

• A keen interest in furthering expertise in data architecture, data modeling, and MLOps capabilities.

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

🏝️ Benefits

• Opportunities for professional development

• Flexible working hours