Data Engineer

Title: Data Engineer Location: Portland, OR or Seattle, WA Duration: Long Term About the Role We’re looking for a rare kind of engineer — someone who doesn’t wait to be told what to do, figures things out, and brings both technical depth and sharp communication to the table. This isn’t a ticket-taker role. You’ll own end-to-end delivery across modern data platforms and full stack applications, work directly with business stakeholders, and be expected to lead technically without hand-holding. If you’re the kind of person who reads documentation on weekends because you’re genuinely curious, explains complex systems clearly to non-technical leaders, and takes pride in clean, reliable pipelines — keep reading. What You’ll Do • Data Platform Engineering (Snowflake & dbt) • Design, build, and maintain dbt models and Snowflake stored procedures powering enterprise data pipelines • Own data transformation logic, CDC processing, curated layer development, and reporting enhancements • Debug pipeline failures, tune performance, and validate data quality end to end • Translate business requirements into robust, scalable technical solutions independently • Application Development (Streamlit) • Build and maintain Streamlit applications used for business reporting and operational monitoring • Develop backend validation processes and analyze AI-generated outputs for accuracy and rule compliance • Debug application failures, trace root causes through logs, and implement lasting fixes • Manage integrations across Streamlit, Snowflake, and backend data services • Real-Time Data Integration (Kafka & Azure) • Work on streaming and real-time data integration pipelines • Troubleshoot ingestion failures, monitor job health, and validate end-to-end data flow • Manage Azure Storage, Event Hub, and secrets/configuration for secure connectivity • Big Data Processing (Databricks & PySpark) • Manage and execute Databricks/PySpark workflows for high-volume use cases • Support ad-hoc processing requests including government reporting pipelines • Validate outputs and ensure successful pipeline execution under deadline pressure • General Data Engineering & Production Support • Provide end-to-end support across APIs, cloud platforms, analytics apps, and data pipelines • Participate actively in enhancement requests, operational maintenance, and issue resolution What You Bring Technical Skills • 7–8+ years of hands-on experience in data engineering • Strong proficiency in Snowflake, dbt, and SQL-based data modeling — this is the core of the role • Proven experience integrating multiple enterprise systems, including SAP and similar source platforms • Working knowledge of Informatica for data integration and pipeline orchestration • Working knowledge of DB2 and experience handling legacy database environments • Hands-on experience with Apache Kafka and real-time/streaming data integration patterns • Experience with Databricks and PySpark for large-scale data processing • Proficiency in Python — required, not negotiable • Experience building Streamlit applications for reporting, monitoring, or operational workflows — required • Practical experience with AI development tooling — including working with LLMs, prompt engineering, AI/ML frameworks, vector databases, and integrating AI-generated outputs into data workflows • Hyperscaler experience with Microsoft Azure as primary; familiarity with AWS or GCP is a plus • Hands-on with Azure services — Blob Storage, Event Hub, Key Vault, and related tooling • Comfortable working across the stack: APIs, pipelines, cloud infrastructure, and front-end tooling The Mindset We’re Hiring For • Self-starter. You don’t wait for someone to define the problem — you go find it • Fast, independent learner. You pick up new technologies, frameworks, and domains quickly and without being told to • High ownership. You treat production systems like they’re yours and take failure personally (in a healthy way) • Detail-oriented. You catch the thing everyone else missed • Intellectually sharp. You can hold complexity in your head and still produce clean, maintainable work Communication & Collaboration • Strong verbal and written communication skills — you can explain a data pipeline to an engineer and a business outcome to an executive, adjusting your language accordingly • Comfortable presenting work, trade-offs, and recommendations to both technical teams and non-technical stakeholders • Collaborative but independent — you work well with others but don’t need to be managed • Experience working in cross-functional environments alongside business analysts, product owners, and leadership Preferred Qualifications • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent professional experience) • Prior experience in automotive, manufacturing, logistics, or similarly data-intensive industries is a plus • Exposure to AI/ML output validation or AI-integrated data workflows • Experience supporting government or regulatory reporting pipelines