Data Engineer Lead

• 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 using Databricks, Spark, Delta Lake, and cloud-native technologies
• Establish data quality, lineage, governance, observability, and monitoring capabilities
• Migrate and modernize 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
• 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 high-quality, reusable, governed data products
• Mentor and coach engineers and foster ownership, continuous learning, accountability, and engineering excellence
• Shape strategic roadmap planning, technology evaluation, and delivery priorities across the ECR portfolio
• Support regulatory, compliance, security, and audit requirements through engineering controls and documentation
• Own complex cross-service problems and facilitate cross-functional collaboration
• Conduct technical interviews and assess engineering talent

• 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, Hadoop, 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 Parquet, Avro, and ORC data formats
• Working knowledge of CI/CD pipelines, infrastructure-as-code, automated testing, and DevOps practices
• Experience with workflow orchestration tools such as Airflow
• Strong understanding of security, privacy, and compliance requirements for sensitive financial and customer data
• Proven ability to lead technical initiatives across multiple teams and influence engineering direction without direct authority
• Excellent communication skills across technical and business functions
• Demonstrated leadership in aligning engineering teams around shared goals and driving delivery through ambiguity
• Ability to influence senior technical and business stakeholders and make thoughtful trade-off decisions
• Knowledge of Java-based application development is a plus
• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related STEM discipline, or an alternative minimum of 10 years of experience in a related field
• Advanced English required

• No benefits or compensation extras explicitly stated