myPOS

• Be the friendly, knowledgeable point of contact for Greek-speaking merchants and business owners across chat, email, and phone • Take ownership of customer issues from first contact through to resolution • Help customers understand their accounts, navigate our products, and get real value from the platform • Act as the customer’s advocate internally, working on their behalf with specialist teams across the business • Explain technical or financial topics, such as payment flows, fees, or account status, in a way that’s easy to understand and free of jargon • Share patterns and feedback from customer conversations with the wider team to help us improve

• Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code

• Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code

• Design, build, and operate scalable, reliable data pipelines and data infrastructure • Ensure high-quality data is accessible, trusted, and ready for analytics and data science • Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code

• Build and maintain MLOps automation end-to-end: CI/CD for models and pipelines, environment management, artifact versioning (models, data, prompts, code), and release governance • Implement and operate model serving infrastructure: deployment patterns (blue/green, canary, shadow), endpoint management, scaling, and latency/throughput optimisation • Build and maintain training and experimentation infrastructure: job orchestration, compute provisioning, experiment tracking, hyperparameter management, and reproducibility tooling • Implement observability for ML systems: data quality checks, feature drift detection, model performance monitoring, bias checks, alerting, and incident response workflows • Build and maintain data pipelines for ingestion, transformation, feature engineering, and export across multiple sources and destinations • Design and maintain a feature store or feature platform layer: serving consistency, point-in-time correctness, and reuse across teams • Expose well-governed datasets, features, and APIs that models, pipelines, and downstream consumers can rely on • Enforce secure data handling and compliance with relevant data protection standards, access controls, and audit requirements • Contribute to documentation, platform standards, and continuous improvement of ML engineering processes across teams

• Build and maintain MLOps automation end-to-end: CI/CD for models and pipelines, environment management, artifact versioning (models, data, prompts, code), and release governance • Implement and operate model serving infrastructure: deployment patterns (blue/green, canary, shadow), endpoint management, scaling, and latency/throughput optimisation • Build and maintain training and experimentation infrastructure: job orchestration, compute provisioning, experiment tracking, hyperparameter management, and reproducibility tooling • Implement observability for ML systems: data quality checks, feature drift detection, model performance monitoring, bias checks, alerting, and incident response workflows • Build and maintain data pipelines for ingestion, transformation, feature engineering, and export across multiple sources and destinations • Design and maintain a feature store or feature platform layer: serving consistency, point-in-time correctness, and reuse across teams • Expose well-governed datasets, features, and APIs that models, pipelines, and downstream consumers can rely on • Enforce secure data handling and compliance with relevant data protection standards, access controls, and audit requirements • Contribute to documentation, platform standards, and continuous improvement of ML engineering processes across teams

• Be the friendly, knowledgeable point of contact for Portuguese-speaking merchants and business owners across chat, email, and phone • Take ownership of customer issues from first contact through to resolution — you’ll follow up, close the loop, and make sure nothing falls through the cracks • Help customers understand their accounts, navigate our products, and get real value from the platform — in plain, clear Portuguese • Act as the customer’s advocate internally, working on their behalf with specialist teams across the business, such as Payments Operations or Account Verification, to get complex cases resolved faster • Explain technical or financial topics, such as payment flows, fees, or account status, in a way that’s easy to understand and free of jargon • Share patterns and feedback from customer conversations with the wider team to help us improve

• Being the friendly, knowledgeable point of contact for French-speaking merchants and business owners – across chat, email, and phone; • Taking ownership of customer issues from first contact through to resolution – you'll follow up, close the loop, and make sure nothing falls through the cracks; • Helping customers understand their accounts, navigate our products, and get real value from the platform – in plain, clear French; • Acting as the customer's advocate internally – working on their behalf with specialist teams across the business (such as payments operations or account verification) to get complex cases resolved faster; • Explaining technical or financial topics – like payment flows, fees, or account status – in a way that's easy to understand and free of jargon; • Sharing patterns and feedback from customer conversations with the wider team to help us improve.

• Lead and support the Business Development Managers in your area • Implement strategic plans to achieve company sales targets • Identify new opportunities for business growth • Provide reports to senior management

• Design, build, and operate scalable, reliable data pipelines and data infrastructure • Ensure high-quality data is accessible, trusted, and ready for analytics and data science • Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code