Top Senior Data Engineer Jobs in Portland, OR

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Build and maintain production data pipelines, dbt models, Airflow workflows, and governed datasets supporting People Analytics. Integrate Workday and other HR systems into reliable Snowflake data products, implement data quality monitoring, row-level security, semantic models, and AI agents. Partner with engineers, analysts, data scientists, and business stakeholders to deliver trusted data solutions, communicate technical trade-offs, enforce governance, and mentor other engineers.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Lead the reverse-engineering and migration of legacy ETL pipelines from SAP BusinessObjects Data Services and JD Edwards into dbt and Databricks architectures. Build automated data validation, maintain SQL Server write-back mechanisms, collaborate with client stakeholders, and lead and mentor a small engineering team. The role requires hands-on expertise in SQL, Python, Spark, orchestration, cloud platforms, and legacy ERP and ETL migrations.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Leads data architecture and engineering modernization across business domains. Designs and implements complex pipelines, integrations, data models, and orchestration using Snowflake, AWS, and Airflow. Establishes data quality, governance, security, testing, observability, CI/CD, and reliability practices. Provides architectural guidance, evaluates technical solutions, leads cross-team initiatives, mentors engineers, and improves platform performance and cost efficiency.

Build and maintain scalable Snowflake-based ELT/ETL pipelines, production data models, and observability frameworks. Translate business requirements into reliable solutions supporting analytics, BI, and ML. Establish data quality, governance, lineage, security, and compliance practices while optimizing warehouse performance, cost, and data freshness. Collaborate with Analytics, Product, and Engineering teams, contribute to architecture decisions, evaluate Snowflake tooling, and use AI-assisted development tools to accelerate delivery.

Designs, builds, and optimizes scalable data collection, storage, access, and analytics systems. Creates and automates reliable data pipelines, supports data quality and governance, ensures SLA and performance compliance, and monitors pipeline operations. The role requires collaboration with analytics, business, and customer teams, with potential work involving AI/ML pipelines, Databricks, DevOps practices, streaming data, and event-driven architectures.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop data warehouse models, transformations, automation, quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop warehouse models, transformations, automation, data quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Designs and maintains a scalable data platform supporting analytics, machine learning, and AI workloads. Builds tooling for reliable data pipelines, semantic layers, data agents, LLM applications, and evaluation systems. Ensures data quality, availability, observability, and reliability while establishing modeling, testing, and platform best practices. Collaborates with Data Science and Product Engineering, leads solutions from design through implementation, and contributes individually in a fast-paced startup environment.

Build and maintain scalable batch and streaming data pipelines, analytics warehouses, transformation layers, and dimensional data models. Perform SQL-based analysis, support dashboards and reporting, monitor data quality and delivery SLAs, troubleshoot incidents, and improve operational reliability. Collaborate with engineers, analysts, data scientists, and business stakeholders while contributing to documentation, governance, observability, and automation.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and data quality processes. Design scalable processing systems, orchestration workflows, storage patterns, and data warehouse solutions. Collaborate remotely, document technical designs, scope projects, and solve complex data engineering and data science problems.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Leads manufacturing analytics and data systems initiatives for a polishing pads factory. Develops Power BI dashboards, KPI scorecards, automated analytical tools, data models, and reporting solutions using cloud platforms and manufacturing systems. Analyzes production and quality data, resolves data-quality and pipeline issues, supports automation improvements, and communicates insights to technical and business stakeholders. Partners cross-functionally on continuous improvement, data governance, digital transformation, and scalable analytics solutions.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.

Leads the design, development, governance, and productization of enterprise data models, pipelines, data products, and ontologies. Builds machine-learning-ready datasets, implements security and lineage controls, optimizes Databricks and Delta Lake environments, establishes data-product standards, collaborates with architects and data scientists, and mentors engineering talent. The role also defines product roadmaps, service levels, usage metrics, and continuous-improvement processes for enterprise data assets supporting AI agents, analytics, and applications.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Designs, builds, and operates scalable batch and streaming data pipelines, ETL/ELT workflows, cloud solutions, and secure Python REST APIs. Develops data foundations for machine learning, generative AI, embeddings, and retrieval-based applications. Ensures data quality, security, observability, lineage, and reliability while optimizing performance and cost. Leads architecture and code reviews, establishes engineering standards, mentors engineers, and collaborates with product, analytics, data science, and machine learning teams.

Develops, architects, and maintains production ETL/ELT and C#/.NET data pipelines across cloud data platforms. Responsibilities include supporting Iceberg, DynamoDB, AWS Lambda, and Trino/Starburst systems; onboarding data sources; tuning performance; monitoring data quality; resolving incidents; coordinating schemas; documenting processes; mentoring engineers; and supporting releases, deployments, architecture, and roadmap decisions.

Build and maintain SQL-based integrations connecting behavioral healthcare EHR/EMR systems to the Eleos platform. Investigate pipeline failures, data discrepancies, and quality issues to identify root causes. Develop reusable extraction, automation, and validation tooling; document data models, mappings, and business logic; and create standardized data models. Collaborate with platform engineers, product teams, and customer technical teams to establish secure access and deliver scalable, reliable integrations.

Design and operate Alpaca’s scalable data platform on GCP, including lakehouse infrastructure, Kubernetes-based deployments, streaming and CDC pipelines, batch ingestion, BI access, cataloging, monitoring, and governance. Build infrastructure as code with Terraform and Ansible, operate distributed query engines and Apache Iceberg, maintain reliability practices, and collaborate with DevOps and analytics teams to support rapidly evolving data requirements.

Own and improve Life360’s Ads Data Engineering platform on Databricks. Build, operate, and optimize batch and streaming pipelines, Delta Lake tables, dbt models, semantic layers, and core metrics. Integrate advertising and business data, implement quality and governance controls, manage AWS infrastructure with Terraform, reduce compute costs, and mentor engineers. Partner with analysts, ad strategists, and sales teams to deliver trusted data products while developing AI-assisted engineering tools and workflows.