Lead, Big Data Analytics & Engineering

About the job

About the Role The Lead, Data Engineering role focuses on data engineering and data unification across diverse enterprise data assets, enabling a single, trusted, and scalable view of data from multiple internal and external sources. This role plays a critical part in supporting the development of innovative, data driven products, services, and actionable insights—particularly in the context of Value Quantification, cyber, and analytics led solutions. As a technical lead, you will provide hands on engineering leadership while partnering closely with Product Management, Data Science, Platform Strategy, and Technology teams to design and deliver high impact data solutions that generate measurable business value. The Role We are seeking a Lead, Big Data Analytics & Engineering who will: Data Engineering & Platform Leadership • Lead the ingestion, aggregation, transformation, and processing of large scale data sets to enable advanced analytics, insights, and downstream consumption. • Design, build, and maintain robust, scalable data pipelines across Hadoop and enterprise data platforms, ensuring data quality, reliability, and performance. • Drive data unification initiatives, integrating multiple structured and semi structured data sources into a cohesive analytical foundation. Cross Functional Collaboration • Partner with Product Managers, Data Science, Platform Strategy, and Technology teams to understand data requirements and translate them into scalable engineering solutions. • Act as a technical bridge between business, analytical, and engineering teams, clearly articulating solution design, trade offs, and implementation approaches. Advanced Analytics Enablement • Manipulate and analyze high volume, high velocity, high dimensional data using modern big data frameworks and analytical techniques. • Analyze large volumes of transactional and product data to generate insights and actionable recommendations that support business growth and value realization. • Apply metrics, measurements, and benchmarking techniques to evaluate solution effectiveness and inform continuous improvement. Innovation & Value Creation • Identify innovative opportunities and deliver proofs of concept, prototypes, and pilots aligned to current and future business needs. • Integrate new and emerging data assets that enhance existing platforms, products, and services and strengthen customer value propositions. • Collect and synthesize feedback from clients, development, product, and sales teams to inform new solutions and product enhancements. Technical Leadership & Mentorship • Provide technical guidance and mentorship to other data engineers and analysts, setting standards for engineering quality, scalability, and maintainability. • Promote best practices in data modeling, pipeline design, performance optimization, and data governance. All About You Technical Skills & Experience • Strong proficiency in Python, including Pandas, NumPy, PySpark, and experience with Impala. • Hands on experience performing data extraction, analysis, and processing on Hadoop based platforms. • Strong SQL skills and experience working with large scale relational and distributed data stores. • Experience with enterprise data platforms and business intelligence ecosystems. • Hands on experience with ETL / ELT and data integration tools such as Airflow, Apache NiFi, Azure Data Factory, Pentaho, or Talend. • Experience with data modeling, querying, data mining, and reporting over large volumes of granular data. • Exposure to machine learning concepts and analytical techniques used in advanced data solutions. • Experience with Graph Databases is a plus. • 8+ years of experience in data engineering, big data analytics, or enterprise data platforms, with at least 2+ years in a technical leadership or lead role. • Experience working with cloud-based data platforms (Azure, AWS, or GCP), including data lakes, distributed compute, and storage services. • Experience implementing CI/CD pipelines and DevOps practices for data engineering workflows. Analytical & Business Acumen • Experience collecting, standardizing, and summarizing diverse data sets, identifying patterns, inconsistencies, and data quality issues. • Strong understanding of how analytical methods and data visualization support business decision making. • Ability to understand complex operational systems and deliver analytics and information products to a large, global user base. Ways of Working • Comfortable working in a fast paced, deadline driven environment, both as an individual contributor and a technical lead. • Ability to seamlessly move between business, analytical, and technical contexts, articulating requirements and solutions to diverse audiences. • Demonstrates Mastercard’s DQ values, with a collaborative, inclusive, and customer centric mindset.

Requirements

• Python
• Hadoop
• Data Engineering
• Data Analytics
• Integration Tools
• Big Data Frameworks

Qualifications

• 8+ years of experience in data engineering
• Experience in technical leadership

Preferred Technologies

• Python
• Hadoop
• Data Engineering
• Data Analytics
• Integration Tools
• Big Data Frameworks

About the company

Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships, and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.