Principal Data Engineer

Key Responsibilities
Platform Architecture & Design
• Lead the architecture, design, and implementation of scalable enterprise data platforms supporting finance modernization initiatives.
• Design modern data lakehouse architectures leveraging Apache Spark, Apache Iceberg, and cloud-native AWS services.
• Define and establish enterprise-wide data engineering frameworks, standards, reusable patterns, and best practices.
• Drive data platform modernization strategies including batch, streaming, and real-time data processing solutions.

Engineering Leadership
• Mentor and guide senior engineers and technical teams across the organization.
• Lead technical discussions, architecture reviews, and strategic modernization initiatives.
• Apply modern software engineering principles, CI/CD practices, and DevOps methodologies within data engineering environments.
• Collaborate with product owners, finance leaders, architects, and cross-functional engineering teams.

Integration & APIs
• Design and develop robust APIs and microservices for enterprise data access and integrations.
• Implement event-driven and streaming architectures using Apache Kafka or equivalent technologies.
• Establish data governance frameworks, lineage tracking, and quality standards across the platform.
• Ensure compliance with regulatory reporting requirements and financial data governance standards.

All About you
• 12+ years of experience in data engineering, data platform development, or related technical roles.
• Proven track record of architecting and delivering large-scale enterprise data platforms in a principal or lead capacity.
• Knowledge of data mesh or data product architectures in enterprise settings.
• Expert-level proficiency in Apache Spark (PySpark) for large-scale data processing.
• Deep hands-on experience with Apache Iceberg or similar open table formats
• Experience designing and implementing streaming architectures using Apache Kafka
• Experience designing and building REST APIs and microservices for enterprise data access.
• Solid understanding of CI/CD pipelines, infrastructure-as-code and DevOps practices.
• Experience with data governance, data quality frameworks, and metadata management tools.