Mastercard is a global leader in the payments industry, dedicated to connecting and empowering an inclusive, digital economy. Our mission is to make transactions safe, simple, smart, and accessible for everyone.

We are seeking a Lead, Big Data Analytics & Engineering to join our team. This senior technical role focuses on data engineering, data unification, and enabling large-scale analytics across enterprise data assets. You will play a crucial part in building a single, trusted, and scalable view of data by integrating diverse internal and external sources.

The role directly supports the delivery of data-driven products, platforms, and insights, particularly in areas like Value Quantification, Cyber Intelligence, and Analytics-led solutions. You will combine hands-on engineering leadership with strong cross-functional collaboration, partnering with Product Management, Data Science, Platform Strategy, and Technology teams to deliver high-impact data solutions that generate measurable business value.

Lead the ingestion, transformation, aggregation, and processing of large datasets to enable advanced analytics. Design, build, and maintain robust, scalable data pipelines on Hadoop and enterprise data platforms, ensuring high standards of data quality and reliability. Drive data unification initiatives, integrating multiple data sources into a cohesive analytical foundation. Manipulate and analyze high-volume, high-velocity, and high-dimensional datasets using modern big data frameworks. Analyze transactional and product data to produce insights and actionable recommendations that support business growth. Apply metrics and measurement frameworks to evaluate solution effectiveness and drive continuous improvement. Partner with Product Managers, Data Scientists, and Technology teams to translate analytical requirements into scalable engineering solutions. Act as a technical bridge, clearly articulating architecture decisions and implementation approaches to diverse stakeholders. Enable alignment across stakeholders to ensure data solutions directly support business and customer outcomes. Identify innovation opportunities, delivering proofs of concept and pilot solutions. Integrate new data assets to enhance existing platforms and strengthen value propositions. Gather and synthesize feedback to inform new solutions and product enhancements. Provide technical leadership and mentorship to data engineers and analysts, setting standards for engineering quality and scalability. Promote best practices in data modelling, pipeline design, performance optimization, and data governance. Influence engineering standards, architectural consistency, and long-term platform sustainability.

You should possess strong proficiency in Python, including Pandas, NumPy, and PySpark, with hands-on experience using Impala. Proven experience working on Hadoop-based platforms for large-scale data extraction, transformation, and processing is essential.

We require strong SQL skills and experience with both relational and distributed data stores, along with experience in enterprise data platforms and business intelligence ecosystems. Hands-on experience with ETL/ELT and data integration tools such as Apache Airflow, Apache NiFi, Azure Data Factory, Pentaho, or Talend is expected.

Your background should include experience in data modelling, querying, data mining, and reporting over large volumes of granular data. Exposure to machine learning concepts and analytical techniques used in advanced data solutions is beneficial. Experience with Graph Databases is a plus.

This role requires 8+ years of experience in data engineering, big data analytics, or enterprise data platforms, including at least 2 years in a lead or technical leadership role. Experience with cloud-based data platforms (Azure, AWS, or GCP), including data lakes, distributed compute, and storage services, is required. Experience implementing CI/CD pipelines and DevOps practices for data engineering workflows is also necessary.

Preferred candidates will have experience enabling GenAI/AI products through scalable data ingestion and transformation pipelines. Exposure to unstructured and semi-structured data processing and building curated datasets for downstream consumption is highly regarded. A strong understanding of data governance, privacy, and security requirements when using enterprise data with AI is crucial.

Furthermore, solid analytical and business acumen is essential. You should have strong experience collecting, standardizing, and summarizing diverse datasets, identifying patterns, inconsistencies, and data quality issues. A solid understanding of how analytics, metrics, and visualization support business decision-making is key. The ability to comprehend complex operational systems and deliver scalable analytics and information products to a global user base is required.

You should be comfortable operating in a fast-paced, delivery-driven environment, acting as both a hands-on contributor and a technical leader. The ability to move seamlessly between business, analytical, and technical contexts, communicating clearly with diverse audiences, is vital. Demonstrating Mastercard’s DQ values, with a collaborative, inclusive, and customer-centric mindset, is fundamental to this role.