DevOps Platform Engineer

Alteryx•Michigan, USA - Remote, MI

5d•$139,475 - $153,900•Remote

About The Position

As a DevOps Platform Engineer within Data Platforms, you will strengthen the standards, automation, and operational practices that enable reliable data product delivery across our enterprise data ecosystem. This role focuses on how data products are developed, tested, reviewed, deployed, monitored, and supported across dbt, Snowflake, GitLab, and related platforms. You will partner with technical data leads to implement and maintain engineering standards that enable centralized Data Engineering, Analytics Engineering, Data Science, and federated Data Ops business teams to deliver trusted production data products consistently and efficiently within our data mesh model. Success in this role means improving engineering consistency, reducing operational friction, increasing release confidence, and helping teams deliver trusted, maintainable data products at scale.

Requirements

5+ years of experience in Data Engineering, Analytics Engineering, DevOps, Platform Engineering, or a related technical discipline.
Hands-on experience supporting production data pipelines, data products, or analytics engineering workloads in an enterprise environment.
Strong working experience with Snowflake, SQL development, dbt, GitLab or similar source control, and CI/CD workflows.
Experience implementing or supporting automated testing, linting, validation, deployment checks, or code quality standards.
Experience with release management, change management, deployment coordination, or production readiness practices.
Familiarity with observability, alerting, monitoring, runbooks, and incident response for production systems or data pipelines.
Ability to troubleshoot complex pipeline, environment, dependency, and data quality issues with minimal guidance.
Strong communication, documentation, collaboration, and problem-solving skills.
BA/BS degree in Information Science, Data Analytics, Computer Science, Software Engineering, a related technical field, or equivalent practical experience.

Nice To Haves

Experience supporting federated data teams, data mesh operating models, or business-owned data product delivery.
Python development or scripting experience for automation, operational tooling, or production support.
Experience supporting productionized Data Science, machine learning, or code-based data products.
Experience with orchestration platforms such as Airflow, Composer, or similar tools.
Familiarity with AWS, GCP, data quality frameworks, lineage tools, data catalogs, observability platforms, or enterprise analytics platforms.

Responsibilities

Implement and maintain reusable engineering patterns, standards, templates, and guardrails for data product delivery across dbt, Snowflake, GitLab, and related platforms.
Maintain code quality practices, including linting, formatting, naming conventions, merge request templates, and review expectations.
Implement and improve CI/CD workflows, including automated testing, validation, deployment gates, promotion logic, and release readiness checks.
Coordinate release and change management activities, including deployment planning, dependency tracking, rollback planning, release notes, and post-release validation.
Operationalize observability and alerting standards for production pipelines, including job health, freshness, failure rates, data quality checks, and operational dashboards.
Support production incidents through triage, root-cause analysis, stakeholder communication, corrective actions, and runbook improvements.
Maintain environment management practices across development, test, and production, including configuration standards, promotion rules, and deployment consistency.
Improve developer experience through documentation, self-service guidance, automation, and streamlined development, review, deployment, and support processes.
Support centralized and federated teams in adopting reusable patterns, quality standards, and operational practices that support trusted data product delivery.