• Pipeline Engineering: Design, build, and maintain scalable ETL/ELT pipelines from diverse sources including Data Lakes, Production ClickHouse instances, flat files, and various APIs. • Infrastructure & Orchestration: Configure and optimize our Data Warehouse infrastructure (ClickHouse) and orchestration layers (Mage.ai). • Engineering Excellence: Implement and manage "engineering-grade" CI/CD workflows, conduct rigorous PR reviews, and ensure robust dependency management across the stack. • Data Modeling & Architecture: Implement Medallion architecture (Bronze/Silver/Gold) and maintain high-performance data models using dbt. • Quality & Observability: Build automated data quality monitoring and alerting; proactively escalate upstream data issues to engineering teams and keep stakeholders informed of pipeline health. • Advanced Data Flows: Develop reverse ETL (rETL) pipelines and expose secure data APIs to enable seamless data consumption across the organization. • Strategic Integration: Manage event streaming and real-time data ingestion (Kafka, CDC) to support high-volume product analytics and tracking.

• The Storage and Data Engineer serves as the enterprise lead for large scale storage and data migrations to AWS, Azure, and Rackspace • Responsible for planning, executing, and validating end to end migration activities using enterprise grade tools and native cloud services while ensuring minimal downtime and full data integrity • Support Dell, NetApp, and cloud native platforms while enabling AI and analytics workloads across multi cloud data lakes • Engagement with AWS S3, Azure Blob Storage, Azure Data Lake, Snowflake storage patterns, and AI data enablement services • Lead planning and execution of enterprise scale storage migrations with minimal downtime • Manage storage presented to Epic EMR platforms and related workloads • Ensure migration activities align to resiliency, compliance, and audit requirements

• Design, develop, and maintain data engineering pipelines using Azure Synapse Analytics and Databricks to support data warehousing and advanced analytics. • Develop interactive and informative dashboards and reports using Power BI for business decision-making. • Implement data integration, data modeling, and ETL/ELT processes, ensuring data quality and consistency across platforms. • Migrate and integrate data from SAP systems (SAP ECC, BPC, APO, SAP BI) to cloud-based environments and analytical tools. • Collaborate with cross-functional teams to understand data requirements and provide data-driven solutions for business needs. • Apply machine learning frameworks and tools for predictive analytics and data science projects. • Ensure data governance, security, and compliance best practices are followed throughout all data processes. • Manage and optimize data lake and data warehousing solutions to support efficient data processing and storage.

Description This is a remote position but applicant must live in Idaho, Utah, Montana, California, Nevada, or Oregon to qualify. Position Overview: The Data Engineer is responsible for designing, building, and operating the downstream data layer that powers analytics, Salesforce enablement, marketing activation, lending insights, automation, and AI initiatives at Frontier Credit Union. This role owns the transformation, modeling, validation, and operationalization of data after it has been ingested into Snowflake. Upstream data sourcing, APIs, and ingestion pipelines are owned by a dedicated Software Engineering function. This role partners closely with that team to define data contracts and ensure ingested data is production-ready for downstream use. Activation platforms include Salesforce CRM, Salesforce Marketing Cloud, internal data applications, and AI-enabled workflows. The focus of this role is ensuring data is trustworthy, well-modeled, and usable by these systems. This position is intended for an experienced Data Engineer who can contribute immediately. We are not seeking an entry-level candidate or someone who requires significant training. The ideal candidate brings strong SQL and Python skills, deep Snowflake experience, and a proven track record of owning production data pipelines and data products. System Ownership & Organizational Impact: - Serves as the primary owner of Snowflake-based transformations and downstream data products. - Translates raw ingested data into trusted, governed, analytics-ready datasets. - Ensures downstream consumers (Salesforce, Marketing Cloud, BI tools, internal apps, and automation workflows) receive consistent and reliable data. - Identifies and resolves data quality issues arising post-ingestion. - Reduces downstream friction by enforcing standards, documentation, and observability across data layers. Essential Functions & Responsibilities - Snowflake Transformations & Data Modeling - Design, build, and maintain Snowflake schemas, tables, views, and transformation layers. - Implement scalable modeling patterns to support CRM, marketing, lending, and operational analytics. - Write and optimize complex SQL transformations with a focus on correctness, performance, and cost efficiency. - Maintain consistent metric definitions and reusable logic across business domains. 2. Downstream Data Pipelines & Orchestration - Build and maintain transformation pipelines that convert ingested data into curated, business-ready datasets. - Implement dependency management, scheduling, and monitoring for downstream workflows. - Support incremental processing, backfills, and reprocessing as business needs evolve. - Partner with upstream engineers to define schema expectations, freshness SLAs, and data contracts. 3. Salesforce & Activation Enablement - Prepare and maintain datasets that support Salesforce CRM and Salesforce Marketing Cloud use cases. - Partner with Salesforce Administrators and Marketing teams to ensure downstream data supports segmentation, analytics, and reporting needs. - Support identity resolution, deduplication logic, and business rules at the data layer. - Ensure consistency between Snowflake-curated data and downstream operational systems. This role supports Salesforce through data products and pipelines, not platform administration or campaign execution. 4. Data Quality, Validation & Observability - Implement data quality checks, validation rules, and anomaly detection for critical datasets. - Monitor data freshness, volume, and schema stability for downstream tables. - Build visibility into data readiness for analytics, automation, and operational use. - Document data models, definitions, and ownership for key datasets. 5. Data Applications & Enablement Tooling - Build internal data tools and lightweight applications (e.g., Streamlit) to support operations, analytics, and decision-making. - Develop tools for pipeline health monitoring, data readiness checks, and operational reporting. - Translate business questions into durable data products rather than one-off analyses. 6. Automation & AI Enablement - Prepare feature-ready datasets to support automation and AI initiatives. - Partner with analytics and AI stakeholders to operationalize models and analytical outputs. - Ensure downstream pipelines support reproducibility, auditability, and long-term maintainability. 7. Operational Discipline & Change Management - Follow established development, testing, and deployment standards for data transformations. - Use version control and documentation standards to support maintainability. - Participate in incident response related to downstream data failures. - Continuously improve reliability, performance, and clarity of data products. Project Collaboration & Communication - Work closely with Software Engineers responsible for data ingestion and APIs. - Collaborate with BI, Salesforce, Marketing, Lending, and Operations teams. - Participate in requirements discovery, design reviews, and prioritization discussions. - Communicate technical tradeoffs and constraints clearly to non-technical stakeholders. Requirements Knowledge, Skills & Abilities Technical Skills - Advanced SQL skills, including complex transformations, window functions, and performance tuning. - Strong Python experience for data pipelines, automation, and data tooling. - Hands-on experience with Snowflake in production environments. - Experience building downstream data pipelines and curated data layers. - Familiarity with orchestration, scheduling, and transformation frameworks. - Experience supporting downstream consumers such as BI tools, CRM platforms, or internal applications. Professional Skills - Strong ownership mindset for data products - Ability to operate independently with clear accountability. - Strong problem-solving and systems-thinking skills. - Clear communication across technical and business audiences. - Comfort working with sensitive data in regulated environments. Education & Experience Requirements Required - Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent professional experience. - 3–6 years of experience in data engineering, analytics engineering, or a closely related role. - Demonstrated experience building Snowflake-based transformations and data products. - Strong production experience with SQL and Python. Preferred - Experience supporting Salesforce CRM or Salesforce Marketing Cloud as downstream consumers. - Experience building internal data tools or applications (Streamlit preferred) - Experience in financial services or other regulated industries. - Experience supporting automation or AI-driven analytics workflows.