Data Platform Engineer

🔥 41 minutes ago

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Paymentology

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2015

💳 Fintech

☁️ SaaS

🏦 Banking

💰 Seed on 2016-02

Fintech • SaaS • Banking

Paymentology is a payments technology company that provides an issuing and processing platform (Lume) enabling banks, fintechs, digital banks, telcos, remittance providers, governments and other organisations to launch, manage and scale card programmes. The platform supports issuing debit, prepaid, credit, hybrid, virtual, numberless cards, multi-currency and crypto/stablecoin-linked cards, tokenization, BIN sponsoring, card processing, fraud control and 3DS. Paymentology positions itself as a partner for fintechs, neobanks, banks, telcos, remitters, lenders, travel and crypto players, emphasizing real-time data, global multi-currency capabilities, and programme management, plus client portal and data intelligence.

📋 Description

• 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

• 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

• Health insurance • Professional development opportunities • Paid time off • Flexible work arrangements