Drive the development of products and platforms with a focus on Machine Learning and Data Engineering.
Design secure, reliable, and scalable solutions that incorporate both functional and non-functional requirements from product teams, security teams, and technology stakeholders. Present these solutions for review within Loyalty Products and to the wider Mastercard Architecture & Technology Architecture Review Team.
Define the technical strategy for Loyalty Data and Predictive Analytics, contributing to long-term goals, roadmaps (12-18 months), and detailed planning at quarterly and sprint levels.
Lead modernization efforts and data transformation solutions for Loyalty Data products, ensuring compliance with payment standards and regulations like PCI-DSS, GDPR, and local On-Soil requirements.
Collaborate with technical leads, data stewards, DBAs, and database developers to support large-scale, like-for-like data migrations.
Stay informed about emerging trends and best practices in big data and cloud platforms, providing technical leadership to enhance the quality of data platforms continuously.
Work closely with teams like Data Privacy, Information Security, Cryptographic Architecture, and Business Operations to gather requirements and ensure compliance with Mastercard policies, industry standards, and regulations.
Translate business requirements into technical specifications involving data streams, integrations, transformations, databases, and data warehouses.
Define the framework, standards, and principles for data architecture, covering areas such as modeling, metadata, security, and reference data.
Evaluate and recommend the best analytics solutions, considering usability, technical feasibility, timelines, and stakeholder needs.
Break down large solutions into smaller, achievable milestones to gather feedback and data from product managers and stakeholders.
Requirements & Skills:
Expertise in data platform engineering concepts, architecture design patterns, and industry best practices.
Subject matter expertise in Databricks and experience in cloud infrastructure, particularly AWS and/or Azure.
Advanced statistical analysis, coding, and data engineering techniques.
Open-source tools, predictive analytics, machine learning, and advanced statistics for basic analytical tasks.
Proficiency in Python, Scala, Spark, Hadoop platforms and tools (Hive, Impala, Airflow, NiFi, Sqoop), and SQL to build Big Data products and platforms.
Experience developing and deploying production-level data applications and workflows/pipelines, including machine learning systems in Java, Scala, or Python, with full lifecycle involvement—from data ingestion to feature engineering, modeling, and evaluation.
Architecting scalable data migration solutions using cloud and data lake technologies, along with data transformation tools like Airflow and NiFi for data pipeline orchestration and SQL Server SSIS for ETL.
Cloud security models, encryption strategies, and network layers for hybrid on-prem and cloud environments.
Curiosity, creativity, and enthusiasm for technology and innovation.
Strong quantitative and problem-solving skills.
An ability to multitask with attention to detail.
Self-motivation, flexibility, and independence in managing your workload.
