Data Engineer Lead

Responsibilities

• 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 workflow orchestration tools like Airflow.
• Optimize large-stable data processing workloads for performance, reliability, scalability, and and cost efficiency.
• Mentor, coach, and raise the technical capability of engineers across the organization.
• Shape strategic roadmap planning, technology evaluation, and delivery priorities.
• Support regulatory, compliance, security, and audit requirements through robust engineering controls and documentation.
• Conduct technical interviews and assess engineering talent.

Requirements

• 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, and streaming architectures.
• Strong understanding of data modeling techniques for analytical and operational workloads.
• Knowledge of CI/CD pipelines, infrastructure-as-code, and DevOps practices.
• Proven ability to lead technical initiatives across multiple teams and influence engineering direction.
• Advanced English proficiency.

Preferred Qualifications

• 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.
• Expertise in Databricks, Apache Spark / PySpark, Hadoop, Airflow, Delta Lake, SQL, Python, Azure Data Services, Kafka/Event Streaming, and GitHub / CI-CD Tooling.
• Knowledge of Java-based application development is a huge plus.

About the Company

Not Specified