• 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.

GitLab is an open-core software company that develops the most comprehensive AI-powered DevSecOps Platform, used by more than 100,000 organizations. Our mission is to enable everyone to contribute to and co-create the software that powers our world. When everyone can contribute, consumers become contributors, significantly accelerating human progress. Our platform unites teams and organizations, breaking down barriers and redefining what's possible in software development. Thanks to products like Duo Enterprise and Duo Agent Platform, customers get AI benefits at every stage of the SDLC. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. An overview of this roleAs a Principal Database Engineer, you’ll design and lead the evolution of the PostgreSQL backbone that powers GitLab.com and thousands of self-managed enterprise deployments. You’ll solve critical challenges around uncontrolled data growth, complex upgrades and migrations, and always-on reliability at global scale, creating the database patterns and platforms that keep GitLab fast, resilient, and cost efficient as usage grows. You’ll architect scalable, distributed database solutions, build proactive health and reliability frameworks, and drive adoption of modern database technologies and data stores that improve both product capabilities and production stability. Working hands-on in the codebase and partnering closely with product and infrastructure teams, you’ll turn long-term database strategy into incremental, customer-visible improvements, shift incident response from reactive to proactive, and help define GitLab’s next-generation data architecture, including sharding and multi-database support. What you’ll do - Lead the architecture and strategy for GitLab.com's PostgreSQL infrastructure, designing scalable, resilient solutions for both SaaS and self-managed deployments. - Build proactive database health and reliability frameworks using continuous monitoring, automated remediation, and predictive analytics to prevent customer-impacting incidents. - Drive database best practices across engineering by guiding schema design, migrations, and query optimization, and by creating self-service tools and guardrails for product teams. - Own end-to-end observability for database systems, designing symptom-based monitoring, leading incident response, and turning learnings into automated, repeatable workflows. - Shape the evolution of GitLab’s database platform by evaluating and implementing modern database technologies and data stores that improve reliability, performance, and product capabilities. - Design solutions and patterns that address uncontrolled data growth, cost efficiency, sharding, multi-database support, and other next-generation data architecture needs. - Collaborate closely with product and infrastructure teams to align product decisions with platform constraints and priorities, breaking down long-term goals into incremental, customer-visible outcomes. - Contribute directly to the codebase to prototype and ship working solutions, maintain technical credibility, and deep-dive into complex production issues when needed. What you’ll bring - Experience architecting, operating, and optimizing PostgreSQL in large-scale, distributed production environments with high availability and disaster recovery requirements. - Deep knowledge of PostgreSQL internals, including the query planner, write-ahead logging, vacuum processes, and storage engine behavior. - Background designing and maintaining highly distributed database platforms with automated failover, robust monitoring, and self-healing capabilities. - Hands-on coding skills and comfort working across the stack, from low-level database and search systems to backend and frontend services. - Familiarity with infrastructure-as-code, GitOps practices, security hardening, and site reliability engineering principles applied to database operations. - Ability to debug complex, cross-system issues, translate findings into durable technical solutions, and turn incident learnings into repeatable automation. - Experience influencing technical direction across multiple teams, providing practical guidance on migrations, query optimization, and database best practices. - Openness to collaborating with people from diverse technical backgrounds, with a focus on clear communication, shared ownership, and learning transferable skills. About the teamData Engineering and Monetization is a function within the Engineering organization with a mission to build a comprehensive foundation of data platforms with responsible data architecture that scales. We focus on the databases and data systems that power GitLab.com and self-managed deployments, partnering closely with product and infrastructure teams across regions in an all-remote, asynchronous way. As part of this group, you’ll help shape how we handle data growth, reliability, and modernization of our database platform, creating patterns and tools that other engineering teams can adopt. For more on how we work, see the Team Handbook Page. The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity. Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary. United States Salary Range $157,900—$338,400 USD How GitLab will support you - Benefits to support your health, finances, and well-being - Flexible Paid Time Off - Team Member Resource Groups - Equity Compensation & Employee Stock Purchase Plan - Growth and Development Fund - Parental leave - Home office support Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know during the recruiting process.