W2 Role || Senior Snowflake Data Engineer – Modern Data Platforms & AI Exposure

Senior Snowflake Data Engineer – Modern Data Platforms & AI Exposure Experience: 8–10 Years Location: Oregon (Local candidates preferred), will consider candidates who can relocate on their own expense looking for a strong Senior Snowflake Data Engineer who can work across multiple areas depending on project needs. Key things they are looking for: Mandatory Skills: • Strong Snowflake experience (hands-on development) • Strong dbt experience (mandatory) • Strong Matillion experience (mandatory) • SQL + Python • Experience building modern cloud data pipelines • Good communication skills • Ability to work independently without heavy guidance • Informatica experience Nice to Have: • Openflow exposure • Azure cloud understanding • AWS concepts understanding • Snowflake Cortex / Snowflake Intelligence exposure • AI / LLM / chatbot / RAG / MCP / Agent concepts exposure Job Summary We are seeking a highly skilled and motivated Senior Snowflake Data Engineer with 8–10 years of experience in designing, developing, and maintaining modern enterprise data platforms and scalable data solutions. The ideal candidate will possess strong expertise in data warehousing, ETL/ELT processes, cloud data engineering, and Snowflake ecosystem technologies, along with foundational exposure to modern AI and AI-enabled data platform concepts. This role will be responsible for building reliable, scalable, and high-performance data pipelines and enabling AI-ready data platforms that support analytics, reporting, operational workloads, and emerging AI/LLM-based use cases across the organization. \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Key Responsibilities • Design, develop, and maintain scalable enterprise data pipelines and modern cloud-based data platforms. • Build robust ETL/ELT workflows for ingesting and transforming data from APIs, databases, flat files, and streaming sources. • Develop and optimize Snowflake-based solutions including stored procedures, tasks, streams, dynamic tables, Snowpark, and performance tuning. • Implement enterprise-scale transformation frameworks using dbt Core and dbt Cloud. • Work with ETL/ELT tools such as Matillion, Informatica, and Openflow (nice to have). • Design and maintain scalable and maintainable data models supporting business intelligence, analytics, and operational reporting. • Develop API-based integrations and data services for enterprise applications and downstream systems. • Ensure data quality, governance, scalability, observability, operational reliability, and cost optimization across the data platform. • Proactively monitor, troubleshoot, and resolve data pipeline and platform issues in production environments. • Collaborate with cross-functional teams including architects, analysts, business stakeholders, and AI/data science teams to deliver enterprise data solutions. • Participate in architecture discussions and contribute to modern data engineering best practices and standards. • Support AI/LLM-enabled initiatives by enabling structured, scalable, and vector-ready data pipelines. • Drive technical ownership of data pipelines and platform reliability across development, deployment, and production support activities. \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Required Skills & Qualifications • 8–10 years of experience in Data Engineering, Data Warehousing, or related roles. • Strong understanding of enterprise data warehousing concepts, dimensional modeling, and scalable data architecture. • Deep understanding and strong hands-on experience with Snowflake architecture, performance optimization, data modeling, security, cost optimization, Snowpark, stored procedures, streams/tasks, dynamic tables, and enterprise-scale data platform implementation. • Strong expertise in SQL and Python programming. • Deep understanding and strong hands-on experience with dbt Core and dbt Cloud including modular transformations, incremental models, testing, documentation, lineage, dependency management, performance optimization, and enterprise-scale ELT best practices. • Hands-on experience with ETL/ELT tools: • Matillion • Informatica