Lead Data Engineer

Role details

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Lead Data Engineer Lead Data Engineer Enterprise Credit Risk (ECR) Who is Mastercard? At Mastercard technology, we work 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. Our decency quotient (DQ) drives our culture and everything we do inside and outside our company. We cultivate a culture of inclusion that respects individual strengths, perspectives, and experiences. We believe our differences enable us to be a better team, driving innovation and delivering better business outcomes. Overview The Enterprise Credit Risk (ECR) team is seeking a Lead Data Engineer to help build and scale the next generation of data platforms that power credit decisioning, portfolio risk management, regulatory reporting, analytics, and AI-driven insights across Mastercard's lending and risk ecosystems. The ideal candidate combines deep hands-on engineering expertise with technical leadership, enabling teams to build reliable, scalable, governed, and high-quality data products. This individual will lead the design and implementation of modern data engineering solutions spanning cloud platforms, large-scale data processing, data governance, and operational excellence. This role will partner closely with Product Management, Risk Analytics, Data Science, Architecture, and Business stakeholders to simplify access to trusted data and accelerate innovation across the ECR program. Role As a Lead Data Engineer, you will: Lead the design, development, and evolution of enterprise-grade data platforms and pipelines supporting credit risk products, decisioning capabilities, and analytics solutions. Architect and implement scalable ETL/ELT frameworks utilizing Databricks, Spark, Delta Lake, and cloud-native technologies. Establish data quality, lineage, governance, observability, and monitoring capabilities to ensure trusted and compliant data products. Drive the migration and modernization of legacy data assets into cloud-based architectures and Data Lakehouse platforms. Partner with Risk, Product, Architecture, and Engineering teams to translate business requirements into scalable technical solutions. Define and promote engineering standards, coding practices, testing frameworks,Data Quality frameworks, deployment automation, and operational excellence across the data ecosystem. Lead technical design reviews and influence architectural direction for data-intensive applications and services. Optimize large-scale data processing workloads for performance, reliability, scalability, and cost efficiency. Enable AI and advanced analytics initiatives through creation of high-quality, reusable, governed data products. Mentor, coach, and raise the technical capability of engineers across the organization by fostering a culture of ownership, continuous learning, accountability, and engineering excellence. Shape strategic roadmap planning, technology evaluation, and delivery priorities across the ECR portfolio, balancing business outcomes, engineering feasibility, risk, compliance, and long-term platform sustainability. Support regulatory, compliance, security, and audit requirements through robust engineering controls and documentation. Own complex problems with dependencies across multiple services and facilitate cross-functional collaboration to drive resolution. Conduct technical interviews, assess engineering talent, and contribute to raising the overall performance bar of the organization. All About You The ideal candidate for this position should have: Essential Skills & Experience Strong expertise in designing and implementing large-scale data engineering solutions and distributed data processing systems. Advanced proficiency with Databricks, Apache Spark, Delta Lake, SQL, and Python. Experience building and operating cloud-based data platforms on Azure, AWS, or GCP. Expertise developing enterprise-grade ETL/ELT pipelines, streaming architectures, and data integration frameworks. Strong understanding of data modeling techniques for analytical and operational workloads. Experience implementing data quality frameworks, lineage, metadata management, and governance practices. Experience with Data formats ( Parquet, Avro, ORC ) Working knowledge of CI/CD pipelines, infrastructure-as-code, automated testing, and DevOps practices. Experience with Workflow orchestration Tools like Airflow Strong understanding of security, privacy, and compliance requirements associated with sensitive financial and customer data. Proven ability to lead technical initiatives across multiple teams and influence engineering direction without direct authority. Excellent communication skills with the ability to collaborate effectively across technical and business functions. Demonstrated leadership in aligning engineering teams around shared goals, driving delivery through ambiguity, and creating clarity for stakeholders across product, risk, analytics, architecture, and operations. Ability to influence senior technical and business stakeholders, make thoughtful trade-off decisions, and guide teams toward pragmatic solutions that improve credit risk outcomes and operational resilience. Knowledge of Java Based application development is a huge Plus. Leade