Lead Data Engineer

Who you are

• The ideal candidate combines deep hands-on engineering expertise with technical leadership, enabling teams to build reliable, scalable, governed, and high-quality data products
• 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
• Lead by influence across engineering, product, risk, analytics, and architecture teams to align priorities and deliver measurable business outcomes
• Create clarity in complex, ambiguous environments by translating business needs into actionable technical direction and execution plans
• Develop engineering talent through mentoring, knowledge sharing, design guidance, and constructive feedback
• Promote a high-accountability culture focused on quality, reliability, security, compliance, and continuous improvement
• Communicate effectively with senior stakeholders and clearly articulate trade-offs, risks, dependencies, and delivery progress
• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related STEM discipline or alternative minimum of 10 years of experience in a related field
• Databricks
• Apache Spark / PySpark
• Hadoop
• Delta Lake
• SQL
• Python

What the job involves

• 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
• 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
• 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

The application process

• End Date: October 10, 2027