Top Data Engineer Jobs in Boise, ID

As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.

As a Data Engineer at Edmunds, you will architect and maintain data platforms for analytics and AI/ML, ensuring scalable, reliable data processing and supporting cross-functional collaboration.

The role involves building large, scalable analytics pipelines, defining data models, integrating data, and collaborating on data architecture and strategies.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Design, build, and maintain data connections and transformations to normalize external portfolio data into a standard Clearwater format. Automate monitoring, improve auto-reconciliation, implement solutions from data feeds, support projects and migrations, mentor hires, and communicate technical details to non-technical stakeholders.

Design, build, and optimize scalable Databricks/Spark data pipelines and data models for healthcare analytics. Monitor pipeline health, troubleshoot production issues, enforce data quality and governance, and collaborate with product and analytics teams to deliver performant, cost-efficient data solutions.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

The Senior Staff Data Engineer will design scalable data systems, lead technical direction, optimize data platforms, and ensure data quality and performance for Observability & Insights.

The role involves designing, building, and maintaining ERP data pipelines, ensuring their reliability and supporting data integration initiatives. Responsibilities include troubleshooting, implementing SDLC best practices, and collaborating with stakeholders to optimize data-related outcomes.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

Build and maintain AWS-based ETL pipelines (Glue/PySpark, MWAA/Airflow, Lambda) to ingest and process terabytes of financial data into an S3 data lake using Iceberg/Parquet. Implement metadata, monitoring, tests (90% coverage), materialized views (Trino/Athena), CloudFormation deployments, documentation, and support agile delivery and operations for federal clients.

Design and build end-to-end geospatial data systems: ingest and process satellite imagery into columnar/chunked stores, expose results via GraphQL/REST and FastAPI, build Next.js/React dashboards, automate with IaC and CI/CD, orchestrate pipelines with Prefect, optimize petabyte-scale I/O, and lead hiring, mentoring, and engineering best practices.

Design, build, and maintain scalable ETL pipelines and data warehouse architecture using BigQuery, Databricks, and GCP. Ingest and transform high-volume data from Postgres, REST APIs, and other sources, improve reporting, create reusable code and tests, and collaborate in an Agile/SCRUM environment.

Design, build, and own Sardine's internal data platform and billing infrastructure. Implement and maintain ETL/ELT pipelines, warehouse partitioning, access controls, and data governance. Partner with stakeholders to define KPIs, produce dashboards, and ensure data quality for executive, GTM, product, and finance teams.

Build and maintain data marts, materialized views, and pipelines in an AWS Aurora PostgreSQL environment. Integrate data with downstream BI platforms (MicroStrategy), design/optimize workflows for analytics and reporting, monitor pipeline health and data quality, and support federal data security compliance (FISMA/NIST 800-53). Collaborate with engineering and product teams to enable data-driven features.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

Design and build scalable data engineering solutions, expand centralized ETL framework and metric definitions, develop production Python and SQL data jobs, use Spark/dbt/Fivetran/Airflow with S3, collaborate cross-functionally, and apply AI coding agents to streamline workflows.

The role involves designing and optimizing data pipelines in Snowflake, developing ETL workflows, collaborating on data integration, ensuring data security, and performance monitoring.

The role involves building and maintaining financial data pipelines in Databricks, bridging Snowflake, and ensuring data accuracy for finance reporting.

As a Data Engineer, you will build and maintain data ingestion pipelines, develop data transformations, and migrate SQL Server processes to Azure-native solutions, supporting marketing campaigns and analytics teams.

Design, build, and maintain Databricks-based data pipelines and ETL/ELT processes using Python and SQL. Optimize workflows for performance and cost, support data modeling and cloud integrations (Azure/AWS/GCP), monitor and troubleshoot pipelines, document best practices, and collaborate with cross-functional teams.

Design, build, and maintain scalable data pipelines and models; collaborate with analysts and data scientists to provision prepared datasets; implement data quality, governance and monitoring; recommend technologies; lead root-cause analysis, code reviews and testing; mentor teammates and support production data environments.

The Snowflake Data Engineer will work on data cleansing and harmonization, moving from legacy systems to SAP S4, focusing on ETL/ELT development and view creation.