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Data Platform Engineer
PaymentologyData Platform Engineer at Paymentology building scalable data infrastructures and engineering robust data pipelines in a high-impact fintech environment.
Tech Stack
Tools & technologiesAirflowAmazon RedshiftApacheAWSBigQueryCloudGoogle Cloud PlatformKafkaKubernetesNoSQLPythonSparkSQLTerraform
About the role
Key responsibilities & impact- 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.
Requirements
What you’ll need- 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 — not just supporting, but owning end-to-end delivery.
- Hands-on experience with modern data engineering tools such as dbt, Apache Airflow or Apache Kafka is required.
- 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 to build data pipelines, automate workflows, and perform data processing.
- Excellent problem-solving skills and the ability to work effectively in a collaborative, fully remote environment.
- A strong inclination to deepen expertise in data architecture, data modelling, and MLOps capabilities.
- Experience with real-time data processing (e.g., Kafka, Spark Streaming) and both SQL and NoSQL data storage solutions is an advantage.
Benefits
Comp & perks- Professional development opportunities
- Flexible working hours