Data Platform Engineer

Paymentology

21h • Remote

About The Position

At Paymentology , we're redefining what's possible in the payments space. As the first truly global issuer-processor, we give banks and fintechs the technology and talent to launch and manage Mastercard and Visa cards at scale across more than 60 countries. Our advanced, multi-cloud platform delivers real-time data, unmatched scalability, and the flexibility of shared or dedicated processing instances. It's this global reach and innovation that sets us apart. We're looking for a Data Platform Engineer to join our Data Engineering team and help build a modern data platform from the ground up. This is a greenfield opportunity focused on designing and implementing scalable data infrastructure, engineering robust data pipelines, and establishing observability, playing a critical role in enabling reliable, high-performance, and secure data systems. You'll work closely with data engineers, analysts, and senior technical stakeholders to design, implement, and operate the foundations of our data stack — from cloud infrastructure and data pipelines to storage and processing layers. This role is ideal for an experienced engineer with strong data platform and cloud infrastructure expertise who thrives in a high-impact, global fintech environment. 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. What it takes to succeed: 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.  At Paymentology , it's not just about building great payment technology, it's about building a company where people feel they belong and their work matters. You'll be part of a diverse, global team that's genuinely committed to making a positive impact through what we do. Whether you're working across time zones or getting involved in initiatives that support local communities, you'll find real purpose in your work — and the freedom to grow in a supportive, forward-thinking environment.

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 — 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.

Nice To Haves

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

Responsibilities

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed