Lead Data Engineer

Join Mastercard's Enterprise Credit Risk (ECR) team as a Lead Data Engineer. In this role, you will design and implement modern data engineering solutions, partner with various stakeholders, and drive the migration and modernization of legacy data assets. You will also mentor and coach engineers, shape strategic roadmap planning, and support regulatory and compliance requirements. The ideal candidate has deep hands-on engineering expertise, experience with cloud-based data platforms, and strong communication skills.

Lead the design and implementation of modern data engineering solutions spanning cloud platforms, large-scale data processing, data governance, and operational excellence.

The ideal candidate combines deep hands-on engineering expertise with technical leadership, enabling teams to build reliable, scalable, governed, and high-quality data products

Delta Lake

SQL

Responsibilities

• 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.
• Expertise developing enterprise-grade ETL/ELT pipelines, streaming architectures, and data integration frameworks
• 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
• Strong understanding of data modeling techniques for analytical and operational workloads
• 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
• Develop engineering talent through mentoring, knowledge sharing, design guidance, and constructive feedback
• Create clarity in complex, ambiguous environments by translating business needs into actionable technical direction and execution plans
• Lead by influence across engineering, product, risk, analytics, and architecture teams to align priorities and deliver measurable business outcomes

Requirements

• Working knowledge of CI/CD pipelines, infrastructure-as-code, automated testing, and DevOps practices
• Experience with Data formats ( Parquet, Avro, ORC )
• Experience building and operating cloud-based data platforms on Azure, AWS, or GCP
• Experience implementing data quality frameworks, lineage, metadata management, and governance practices
• 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
• Excellent communication skills with the ability to collaborate effectively across technical and business functions
• 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
• 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
• 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
• Python

Preferred

• Experience with Workflow orchestration Tools like Airflow