Key Responsibilities
Data Engineering & Development
• Design, develop, test, and deploy high-quality, secure, scalable, and resilient data pipelines using Apache Spark, Java/Scala across Hadoop and cloud-native object storage platforms.
• Build and maintain batch and near real-time data processing frameworks capable of supporting petabyte-scale workloads.
• Develop reusable engineering components and frameworks that accelerate data product delivery while maintaining enterprise standards.
Architecture & Platform Engineering
• Design and implement a “build once, run anywhere” architecture supporting seamless deployment across on-premises and public cloud environments without code changes.
• Implement data lineage, metadata management, data cataloguing, data quality controls, and observability capabilities across the data ecosystem.
• Collaborate with architects and platform teams to establish scalable design patterns and engineering best practices.
Cloud Modernisation
• Contribute to migration initiatives moving legacy ETL and data warehouse workloads from on-premises environments to cloud-native architectures.
• Leverage cloud services such as Amazon S3, EMR, Glue, and related data services to improve scalability, reliability, and operational efficiency.
• Drive adoption of modern lakehouse and distributed compute architectures.
Delivery & Technical Leadership
• Lead end-to-end development activities including requirement analysis, solution design, coding, testing, deployment, and production support.
• Mentor and guide junior engineers through code reviews, technical coaching, and engineering best practices.
• Partner with product owners, analysts, architects, and business stakeholders to deliver high-quality solutions within committed timelines.
Operational Excellence
• Troubleshoot complex production incidents and perform root cause analysis to identify and implement long-term remediation strategies.
• Ensure compliance with Mastercard’s engineering, security, quality assurance, and operational governance standards.
• Continuously identify opportunities to improve performance, automation, monitoring, and process efficiency.
Innovation
• Evaluate emerging data technologies and conduct proof-of-concept (POC) initiatives to determine their applicability within Mastercard’s data ecosystem.
• Contribute to engineering innovation and continuous improvement initiatives across the organisation.