Principal Data Engineer (Remote - US)

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Principal Data Engineer in the United States.

In this role, you will lead the architecture and development of a next-generation data and AI platform, handling petabyte-scale data from multiple high-volume SaaS products. You will design scalable pipelines, semantic layers, and AI-driven data quality systems that power critical reporting and analytics. The position offers the opportunity to influence data strategy, mentor senior engineers, and implement cutting-edge AI solutions for financial and e-commerce applications. You will collaborate across teams to translate complex requirements into auditable, high-performance data models while driving innovation in data operations and AI-assisted workflows. The environment encourages technical leadership, problem-solving, and creating systems that impact both internal analytics and customer-facing solutions.

Accountabilities

• Lead end-to-end architecture for data ingestion, transformation, modeling, and serving across multiple SaaS products at petabyte scale.
• Design and maintain semantic layers, DBT models, metrics, and reporting systems for both customer-facing and internal analytics.
• Build and deploy AI agents for data operations, ensuring high-quality data pipelines, automated model maintenance, and semantic mapping validation.
• Develop and enforce data quality measures, monitoring, anomaly detection, and lineage tracking integrated with CI/CD and orchestration systems.
• Oversee LLM data preparation, curation, and secure pipelines for fine-tuning and retrieval-augmented workflows.
• Establish domain-driven standards, including data contracts, ownership, SLAs/SLOs, and coaching teams on best practices.
• Optimize system performance and cost, including compute patterns, partitioning, caching, and materialization strategies.
• Collaborate with product, compliance, and engineering teams to convert regulatory and business requirements into durable, auditable data models.
• Mentor senior engineers, conduct design reviews, and lead initiatives to improve code quality, testing, and platform reliability.

• 10+ years of hands-on experience in data engineering and architecture, including Snowflake and DBT at scale.
• Expertise in SQL, Python, and building AI/LLM systems for data operations and quality management.
• Proven experience creating domain-oriented semantic layers and metrics stores for both external compliance reporting and internal analytics.
• Strong knowledge of data quality, observability, profiling, anomaly detection, and integration into CI/CD pipelines.
• Experience with distributed data processing and streaming systems (Spark, Flink, Kafka/Kinesis) and modern orchestration tools (Airflow, Dagster, Prefect).
• Practical experience with ML/MLOps, model lifecycle, monitoring, drift management, and governance.
• Understanding of security, privacy, and compliance requirements for financial and tax data (SOC 2, ISO 27001, GDPR/CCPA).
• Ability to mentor teams, communicate complex concepts clearly, and influence technical strategy.
• Comfortable presenting executive-level data stories and guiding technical decision-making.