Senior Data Engineer

Trulieve is one of the fastest growing companies in the nation, dedicated to providing patients with natural relief through their products. They are seeking a Senior Data Engineer to design and implement data architectures on Snowflake, lead the development of complex data pipelines, and ensure data governance and compliance standards are met.

Responsibilities:

• Design and Implement Snowflake-Native Data Architectures
• • Lead the creation and optimization of modern data architectures on Snowflake, including multi-layer pipelines (bronze/silver/gold), real-time streaming ingestion, and governed analytics layers
• • Ensure that the architecture supports high-volume data workloads, near-real-time freshness requirements, and integrates seamlessly with upstream operational systems and downstream analytics consumers
• Lead the Development of Complex, End-to-End Data Pipelines Using Native Snowflake Services
• • Architect and build data pipelines using Dynamic Tables for declarative SQL-based transformations with automated dependency management and incremental refresh
• • Implement real-time and near-real-time ingestion using Snowpipe and Snowpipe Streaming
• • Design event-driven and procedural pipeline logic using Streams and Tasks for complex orchestration scenarios (MERGE, SCD patterns, external function calls)
• • Leverage Snowpark (Python) for advanced transformations that require procedural logic beyond SQL
• Collaborate with Data Scientists, Analysts, and Other Stakeholders
• • Work closely with analytics and data science teams to understand data requirements and translate them into scalable, performant Snowflake solutions
• • Build and maintain Semantic Views and governed consumption layers that provide self-service access to curated data
• • Provide technical guidance on Snowflake best practices for data usage, cost optimization, and warehouse sizing
• Ensure Data Security, Governance, and Compliance Standards Are Met
• • Implement and manage Snowflake data governance features including Dynamic Data Masking, Row Access Policies, Object Tagging, and Data Classification
• • Establish and maintain data governance frameworks ensuring data quality and compliance with relevant regulations (SOX, GDPR, HIPAA)
• • Manage role-based access control (RBAC), data sharing, and cross-account governance using Snowflake's native security model
• Lead the Technical Design of New Projects
• • Responsible for making critical decisions regarding Snowflake architecture patterns, product direction, and delivery tradeoffs, including when to use Dynamic Tables, Streams/Tasks, Materialized Views, and external orchestration based on business requirements and user needs
• • Develop and enforce best practices for pipeline development, testing, deployment, and monitoring within the team
• • Design and document data contracts and SLAs for pipeline freshness (TARGET_LAG), data quality, and availability
• Mentor and Develop Junior Engineers
• • Provide technical leadership and mentorship to junior and mid-level engineers, fostering a culture of continuous learning and improvement
• • Lead code reviews, pair programming sessions, and technical workshops focused on Snowflake-native development patterns and engineering best practices
• • Create reusable patterns, templates, and documentation for common pipeline scenarios (CDC ingestion, SCD management, real-time aggregations)
• Operate as a Product Minded Data Engineering Leader
• • Partner with business stakeholders, analytics teams, and engineering leaders to shape the roadmap for data products and platform capabilities based on business value, user needs, and operational priorities
• • Translate ambiguous business problems into clearly defined product requirements, success metrics, delivery plans, and prioritized engineering work
• • Own the lifecycle of key data products and shared platform services, including intake, prioritization, stakeholder alignment, adoption, and continuous improvement
• Continuously Evaluate and Improve Existing Systems
• • Regularly review current data systems to identify opportunities for migration to modern patterns (for example, migrating legacy Streams/Tasks to Dynamic Tables where appropriate)
• • Monitor pipeline health, refresh performance, and cost efficiency using Snowflake's INFORMATION_SCHEMA, ACCOUNT_USAGE, and alerting capabilities
• • Implement optimizations including incremental refresh tuning, warehouse right-sizing, and query performance improvements

Requirements:

• Typically requires 7+ years of experience in data engineering or related fields
• Proven track record of designing and implementing large-scale data solutions on Snowflake or similar cloud data platforms
• 2+ years of hands-on experience with Snowflake-native pipeline services (Dynamic Tables, Streams/Tasks, Snowpipe)
• Deep Understanding of Data Engineering Concepts
• Extensive knowledge of data modeling, including designing and maintaining relational, dimensional, and semi-structured data models within Snowflake
• Proficiency in data warehousing concepts with hands-on experience designing multi-layer transformation pipelines (staging, intermediate, marts)
• Strong understanding of incremental processing patterns, change data capture (CDC), and slowly changing dimension (SCD) strategies
• Expertise in Snowflake Platform and Native Data Pipeline Services
• Deep proficiency with Snowflake Dynamic Tables (TARGET_LAG, REFRESH_MODE, pipeline dependency graphs, incremental vs. full refresh)
• Hands-on experience with Snowpipe and Snowpipe Streaming for continuous and real-time data ingestion
• Strong knowledge of Snowflake Streams and Tasks for event-driven and procedural pipeline orchestration
• Experience with Snowpark (Python/Scala) for complex data transformations and UDFs/UDTFs
• Familiarity with Snowflake Cortex AI functions for embedding AI/ML capabilities into data pipelines
• Proficiency in SQL, Python, and Data Engineering Frameworks
• Advanced SQL skills including window functions, CTEs, recursive queries, semi-structured data handling (VARIANT, OBJECT, ARRAY), and performance optimization
• Python proficiency for Snowpark development, automation scripting, and integration work
• Experience with modular SQL transformation development, testing, and documentation using native Snowflake patterns and shared engineering standards
• Familiarity with orchestration platforms such as Apache Airflow / Astronomer for pipeline scheduling and monitoring
• Experience with Real-Time Data Processing and Streaming Architectures
• Hands-on experience designing solutions for real-time and near-real-time data pipelines using Snowpipe Streaming and Kafka connectors
• Understanding of event-driven architectures and their integration with Snowflake's continuous data pipeline features
• Experience with change data capture (CDC) patterns and tools (Debezium, Fivetran, custom CDC)
• Strong Knowledge of Cloud Infrastructure and Cost Optimization
• Expertise in cloud platforms (AWS, Azure, or GCP) with a focus on integration with Snowflake (external stages, storage integrations, PrivateLink)
• Experience with Snowflake cost management including warehouse sizing strategies, auto-suspend/resume, resource monitors, and query optimization
• Familiarity with infrastructure-as-code tools (Terraform, Pulumi) for managing Snowflake resources declaratively
• Lead the Technical Design of New Projects
• Responsible for making critical decisions regarding Snowflake architecture patterns, product direction, and delivery tradeoffs, including when to use Dynamic Tables, Streams/Tasks, Materialized Views, and external orchestration based on business requirements and user needs
• Develop and enforce best practices for pipeline development, testing, deployment, and monitoring within the team
• Design and document data contracts and SLAs for pipeline freshness (TARGET_LAG), data quality, and availability
• Mentor and Develop Junior Engineers
• Provide technical leadership and mentorship to junior and mid-level engineers, fostering a culture of continuous learning and improvement
• Lead code reviews, pair programming sessions, and technical workshops focused on Snowflake-native development patterns and engineering best practices
• Create reusable patterns, templates, and documentation for common pipeline scenarios (CDC ingestion, SCD management, real-time aggregations)
• Operate as a Product Minded Data Engineering Leader
• Partner with business stakeholders, analytics teams, and engineering leaders to shape the roadmap for data products and platform capabilities based on business value, user needs, and operational priorities
• Translate ambiguous business problems into clearly defined product requirements, success metrics, delivery plans, and prioritized engineering work
• Own the lifecycle of key data products and shared platform services, including intake, prioritization, stakeholder alignment, adoption, and continuous improvement
• Continuously Evaluate and Improve Existing Systems
• Regularly review current data systems to identify opportunities for migration to modern patterns (for example, migrating legacy Streams/Tasks to Dynamic Tables where appropriate)
• Monitor pipeline health, refresh performance, and cost efficiency using Snowflake's INFORMATION_SCHEMA, ACCOUNT_USAGE, and alerting capabilities
• Implement optimizations including incremental refresh tuning, warehouse right-sizing, and query performance improvements
• Preferred certifications include SnowPro Advanced: Data Engineer, SnowPro Core, or AWS/Azure/GCP data engineering certifications
• Continuous learning through relevant certifications and training to stay current with Snowflake platform releases and modern data engineering practices
• Familiarity with Snowflake's release cycle and ability to evaluate new features (for example, Cortex AI, Document AI, Iceberg Tables) for team adoption