Top Data Engineer Jobs in Spokane, WA

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Designs and leads scalable data platforms, event-driven services, orchestration pipelines, and DataOps practices using Python, Airflow, Snowflake, Kafka, and related technologies. The role establishes engineering standards, governance, security, observability, cost efficiency, and AI-assisted development practices. It also mentors engineers, drives cross-functional initiatives, modernizes data ingestion, and ensures reliable, compliant, and well-governed systems.

Lead database engineering for a federal species-data integration program in a hybrid AWS cloud environment. Responsibilities include harmonizing schemas, documenting source-to-target mappings and lineage, enforcing metadata and data-quality standards, implementing encryption and NIST-aligned security controls, building authoritative source registries, optimizing performance, and leading production migration, validation, monitoring, and operational documentation. The role requires Databricks or equivalent lakehouse expertise, ETL programming, federal governance knowledge, and database migration leadership.

Designs secure Azure data pipelines and analytics foundations using Databricks and ADLS Gen2. Responsibilities include modernizing Oracle workloads, developing medallion data models, implementing data quality and monitoring practices, integrating curated datasets with Power BI, and establishing secure Azure Government identity, network, storage, and access patterns. The role also requires documentation, operational support, stakeholder collaboration, and performance optimization.

Leads data engineering strategy and mentors an analytics engineering team. Designs scalable ELT pipelines, warehouse integrations, data models, API ingestion workflows, and data quality frameworks using cloud and database technologies. Conducts exploratory analysis, supports dashboards and analytical solutions, monitors warehouse performance and costs, and ensures data governance, security, and compliance. Collaborates with data scientists, analysts, engineers, and business stakeholders to deliver healthcare analytics solutions.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

Lead the data engineering strategy and architecture for distributed pharmaceutical data systems. Design and scale pipelines, APIs, and data platforms while ensuring reliability, governance, quality, and PII/PHI protection. Drive technical decisions, improve developer efficiency, deliver business impact, influence cross-functional roadmaps, mentor engineers, communicate with executives, and promote organizational adoption of new technologies and processes.

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.

Develop business intelligence and analytics solutions using financial, operational, RF, and video performance data. Build scalable data models, dashboards, reports, automation workflows, and ETL/ELT processes using Power BI, Tableau, DataMiner, and Qligent. Analyze trends and anomalies, monitor satellite and video delivery performance, translate business requirements into actionable insights, and collaborate with enterprise stakeholders in a fully remote consulting role.

Design, build, and operate scalable cloud-native data platforms and pipelines supporting analytics, reporting, AI, and customer-facing applications. Develop ETL/ELT workflows, data lakes, event-driven streaming solutions, integrations, and data models using Python, SQL, AWS, databases, warehouses, messaging technologies, and orchestration tools. Monitor production systems, optimize performance, automate testing and deployment, and collaborate with engineering, platform, product, and data teams.

Design, build, and operate scalable cloud-native data platforms and pipelines supporting analytics, reporting, AI workloads, and customer-facing applications. Develop ETL/ELT processes, data lakes, event-driven and streaming solutions, data models, enterprise integrations, and automated monitoring and deployment systems. Collaborate with software, platform, product, and data teams to ensure reliable, secure, high-performance production systems.

Designs and builds production data systems, pipelines, database schemas, analytical models, and internal platforms. Responsibilities include writing tested code, optimizing queries and storage, controlling infrastructure costs, enforcing CI/CD, and implementing data testing, monitoring, and alerting. The role also develops AI and LLM-backed data use cases, including retrieval pipelines, embeddings, vector storage, evaluation harnesses, and agentic workflows.

Build and maintain ETL and data pipelines using Python, PySpark, and AWS services. Orchestrate workflows, develop event-driven integrations, optimize data storage and queries, process API and JSON data, and implement data quality monitoring. Support CI/CD, production operations, cloud migration, data lake and lakehouse initiatives, and near-real-time systems while collaborating with stakeholders to deliver technical solutions.

Build and maintain ingestion pipelines, Python services, and dbt models on AWS and Snowflake. Own operational on-call responsibilities, migrate pipelines to Airflow, contribute to CI/CD, collaborate with BI/Analytics, and mentor junior teammates.

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

Architect, build, and maintain Snowflake-based ELT pipelines and production data models. Implement observability, data quality, and governance; optimize cost and freshness. Partner with Analytics, Product, and Engineering, use dbt/CI-CD and AI-assisted tooling, and participate in reviews and architecture planning to support analytics, BI, and ML.

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.

Design, build, and maintain operational and analytical data platform capabilities using Java Spring Batch, Python, AWS, Oracle, and Snowflake. Deliver end-to-end development, production rollout, and support; improve financial product data quality, modeling, and SQL performance; collaborate with business partners and squads.

Design, build, and maintain scalable batch and real-time data pipelines and platform components. Produce curated datasets, ensure data quality, observability, and lineage, and collaborate cross-functionally to deliver data products that support analytics, experimentation, and ML.

Build and maintain production data pipelines and models using SQLMesh, BigQuery, and Python. Improve observability, reliability, and performance; support finance and revenue pipelines; participate in on-call rotation and own features end-to-end.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

The Principal Data Engineer will lead data architecture initiatives, design scalable data systems, and oversee data standards across teams while ensuring high-quality data infrastructure and guiding engineering teams.

Sets technical direction and architectural coherence across Netflix’s Ads Data Engineering organization. The role guides cross-domain data system decisions, partners with engineering and business leaders, mentors data engineers, shapes platform-wide initiatives, and advances AI-native data engineering. Responsibilities span modern data foundations, ad-tech data flows, privacy and governance, platform migrations, scalable data products, and collaboration with Ads Platform Engineering, Data Science, and Product teams.

Build and operate ingestion and transformation pipelines to turn messy insurance data into clean datasets. Design bronze/silver/fact layers, model and tune Postgres and analytical stores, define KPIs and reconciliation checks, orchestrate observable data jobs, and ensure end-to-end data quality for analytics and AI products.