Principal Data Engineer

Design end-to-end data architectures, frameworks, and pipelines that support large-scale and complex data ingestion, processing, transformations, reporting, and analytics. Collaborate with stakeholders to ensure alignment with business and product strategies. Build and optimize robust ETL/ELT workflows, data models, and distributed data processing jobs. Implement best-in-class solutions using technologies such as Python, SQL, dbt, DMS, AWS Glue, Airflow, Docker/Kubernetes, Snowflake, and other cloud-native services. Develop best practices to ensure data quality, integrity, lineage, governance, performance, and reliability across all pipelines, data platforms, and analytics applications. Establish and maintain SLAs, monitoring, and alerting for mission-critical data services with observability design and implementation. Identify and resolve performance bottlenecks to ensure scalable and cost-efficient data processing. Provide technical leadership and mentorship to senior and junior engineers across the organization. Promote engineering excellence and best practices in data engineering and developer experience —including CI/CD, infrastructure as code (IaC), automated testing, observability, security/privacy, and AI assistant tools. Lead complex problem-solving initiatives and guide decisions on tools, technologies, and design patterns. Partner with data engineers, software engineers, QA, product managers, customer success, and business leaders to define data requirements and deliver high-impact solutions.