
201 - 500 employees
Dive into a new world of payments with myPOS to find out how the latest payment technologies will grow your business.
🔥 0 minutes ago
Improve your chances of getting an interview by checking your resume score before you apply.

201 - 500 employees
Dive into a new world of payments with myPOS to find out how the latest payment technologies will grow your business.
• 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
• Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience) • 6+ years of experience as a Data Engineer, building and maintaining production-grade pipelines and datasets • Strong Python and SQL skills with a solid understanding of data structures, performance, and optimization strategies • Hands-on experience with orchestration (like Airflow, Dagster, Databricks Workflows) and distributed processing in a cloud environment • Experience with analytical data modeling (star and snowflake schemas), DWH, ETL/ELT patterns, and dimensional concepts • Experience building reliable incremental data ingestion pipelines from DBs and APIs. • Familiarity with at least one major cloud provider (GCP, AWS, Azure) and deploying data solutions in the cloud • Familiarity with CI/CD for data pipelines, IaC (Terraform), and/or DataOps practices • Strong troubleshooting mindset: ability to debug issues across data, infra, pipelines, and deployments • Collaborative mindset and clear communication across engineering, analytics, and business stakeholders
• Excellent compensation package • myPOS Academy for upskilling and training • Unlimited access to courses on LinkedIn Learning • Refer a friend bonus as we know that working with friends is fun • Teambuilding, social activities and networks on a multi-national level
Apply Now