Top Tech Jobs & Startup Jobs

Own product workstreams for Mastercard’s Test & Learn experimentation platform, using customer research, market insights, analytics, and stakeholder feedback to shape roadmap decisions. Partner with engineering, design, sales, marketing, and regional teams to deliver scalable capabilities, improve go-to-market readiness, and drive adoption. Identify AI opportunities, support product positioning and launches, advise market-facing teams, and mentor junior product professionals.

Designs, develops, tests, and supports scalable, cloud-native experimentation platforms and analytics-driven applications. Responsibilities include leading team-level system design, building distributed services and APIs, optimizing Databricks and Spark workloads, troubleshooting performance and reliability issues, and translating business needs into secure technical solutions. The engineer collaborates across teams, contributes throughout the software lifecycle, improves CI/CD and observability practices, and mentors junior and mid-level engineers.

Leads Southeast Asia receiving-network commercialization for cross-border remittances. Expands payout coverage, acquires and manages banks, fintechs, wallets, and receiving service providers, negotiates commercial agreements, and improves transaction performance, pricing, service quality, and partner economics. Develops remittance corridors, supports push-to-card and account or wallet payouts, monitors market and regulatory trends, and collaborates with Product, Operations, Compliance, Technology, Finance, and Treasury teams.

Provides senior individual-contributor technical leadership for complex, enterprise-scale software systems. Leads architecture, design, development, testing, deployment, operational readiness, and production issue resolution across distributed platforms. Partners with product, business, architecture, and engineering teams to deliver secure, scalable, resilient solutions; drives automation, engineering standards, technical roadmaps, reliability, performance, and developer productivity. Influences cross-functional initiatives, mentors engineers, and contributes to architecture forums and engineering communities.

Leads the design, development, deployment, and evaluation of machine learning solutions, including forecasting, recommendation, propensity, fraud, and customer analytics models. Defines experimentation frameworks, applies foundation-model embeddings, measures business impact, and partners with product, engineering, analytics, and business teams. Mentors data scientists and AI engineers, presents technical recommendations to leadership, supports hiring, and shapes the organization’s long-term technical direction.

Leads development of secure, scalable big data pipelines and data engineering solutions using Spark, Scala, Python, Java, Hadoop, object storage, and NiFi. Drives data ingestion, movement, access automation, engineering standards, and process improvements. Collaborates with stakeholders and distributed teams, resolves production incidents, performs root-cause analysis, evaluates new technologies, and provides technical leadership to junior engineers.

Provides senior technical leadership as an individual contributor, designing and delivering complex enterprise-scale software across distributed, secure, and resilient systems. Leads architecture, development, testing, deployment, operational excellence, automation, and production issue resolution. Influences technical strategy, engineering standards, platform roadmaps, and cross-functional initiatives while mentoring engineers and communicating recommendations to senior stakeholders.

Drive MPGS and Acceptance revenue growth across EEMEA by identifying new business, building merchant and FI relationships, developing and executing sales strategies, conducting market research, leading proposals and negotiations, collaborating cross-functionally to deliver products, and tracking pipeline and sales performance.

Designs and maintains scalable data pipelines, data quality frameworks, and cloud-native data solutions using Spark, Databricks, Hadoop, AWS, and advanced SQL. Implements validation, monitoring, observability, and automated remediation capabilities. Applies AI/ML, anomaly detection, and statistical modeling to improve data quality. Provides production support, resolves complex incidents, contributes to architecture and engineering standards, and collaborates with cross-functional data and engineering teams.

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive models, recommendation systems, forecasting, embeddings, and foundation-model applications. Defines experimentation frameworks and success metrics, measures business impact, partners with cross-functional stakeholders, presents findings to leadership, and mentors data scientists and AI engineers. The role also contributes to hiring, capability development, and the technical direction of the AI organization.