Principal Data Engineer

Primary Responsibilities:

Data Architecture & Canonical Model Design
Design, build, and maintain canonical data models that serve as the single source of truth across analytics and AI use cases
Define and enforce data contracts between upstream systems and downstream consumers
Handle schema evolution, versioning, and drift management proactively
Ensure alignment between business semantics and physical data models

Data Engineering & Pipeline Development
Build scalable and efficient data pipelines using Snowflake, SQL, and Python
Process both structured and semi-structured data (JSON, logs, API payloads)
Optimize transformations for performance, cost, and scalability
Implement reusable, modular pipeline components

Advanced Data Modeling for Analytics
Design dimensional and normalized data models for reporting, ML, and AI workloads
Optimize data models for BI tools, self-service analytics, and LLM consumption
Develop metric-layer ready models to ensure consistency across reporting

Data Governance & Quality
Implement data validation, monitoring, and quality checks across pipelines
Build frameworks to detect schema drift and data inconsistencies
Ensure adherence to data governance, lineage, and auditability standards
Support compliance requirements (PHI/PII handling, access control, traceability)

AI/ML & GenAI Enablement
Structure data to support RAG pipelines, embeddings, and LLM-based applications
Enable feature-ready datasets for ML and AI use cases
Collaborate with AI/ML engineers to ensure data readiness for agentic workflows

Performance Optimization & Platform Engineering
Optimize Snowflake performance (clustering, partitioning, query tuning, cost management)
Build frameworks for data observability, monitoring, and alerting
Improve pipeline reliability, scalability, and fault tolerance

Required Qualifications:

Bachelor's degree in Computer Science, Engineering, Data Engineering, or a related technical field (or equivalent practical experience)
12+ years of overall experience in software engineering and data engineering roles, with significant experience designing and delivering large scale data platforms in enterprise environments
Proven expertise in with Snowflakes and Databricks.
Solid hands on experience with cloud based data platforms (Azure and/or GCP), including data storage, processing, orchestration, and monitoring services
Deep experience with ETL/ELT frameworks, batch and streaming data processing, and distributed data systems
Experience collaborating with Analytics, BI, Data Science, and Product teams to deliver trusted, reusable, and performant data assets
Proven expertise in data engineering architecture and solution design, including building, optimizing, and scaling high volume, high availability data pipelines
Advanced proficiency in SQL and at least one programming language such as Python for data pipeline and platform development
Solid knowledge of data quality, data observability, lineage, and metadata management, and implementing governance controls in enterprise data ecosystems
Demonstrated ability to work across cloud and on prem ecosystems, supporting hybrid data architectures at scale