Jobgether is hiring a Principal Data Engineer to lead the architecture and development of our next-generation data and AI platform. You will design scalable pipelines and semantic layers that handle petabyte-scale data from multiple SaaS products, powering critical analytics for financial and e-commerce applications.

What You'll Do

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.

What We're Looking For

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.

Technical Stack

Snowflake, DBT, SQL, Python
AI/LLM systems
Spark, Flink, Kafka/Kinesis
Airflow, Dagster, Prefect

Team & Environment

You will collaborate across teams with product, compliance, and engineering.

Benefits & Compensation

Competitive base salary, with ranges varying by location and potential annual performance bonuses.
Remote work flexibility while collaborating with high-performing teams.
Comprehensive health and wellness benefits, including medical, life, and disability insurance.
Paid time off and parental leave.
Inclusive culture with multiple employee resource groups supporting diversity and equity.
Opportunities for professional growth, skill development, and mentorship in an AI-first environment.
Engagement in high-impact projects with global reach, influencing both product and operational analytics.