POSITION: Data Pipeline Release Engineer (Azure DevOps & Azure Data Factory)
LOCATION: 100% Fully Remote (EST working hours)
DURATION: 6-12 Month-Contract (eligible for extensions)
PAY RANGE: $70.00 - $74.00 per hour
REQUIRED SKILLS & EXPERIENCE:
- 8+ years in release engineering & build automation (strong Azure expertise is required!).
- Data Release Focus Experience: Deep understanding of promoting data pipelines, monitoring releases, tagging, cost tracking, and operational reporting for release health.
- Advanced skills with Azure Data Factory (ADF), Databricks and Snowflake code promotions across multiple environments.
- Hands-on with version control and build tools like Git, Azure DevOps Pipelines (ADO), GitHub Actions.
- Experience with Azure Services to Support Releases: Azure Container Registry (ACR), Logic Apps, Nets and NSGs, Virtual Machines, Azure Storage, Managed Identities, Key Vault, Load Balancer or Application Gateway, AKS fundamentals.
- Experience building reusable release templates and task libraries in YAML-based pipelines and GitHub Actions.
- Scripting/programming in Python and PowerShell for automation and tooling.
- Knowledge of rollback and recovery patterns including pre-deployment backups and hotfix flows.
- Familiarity with containerized build and release tasks and secret management via Key Vault or managed identities.
PLUSSES:
- Azure Certifications: Azure Fundamentals (AZ-900) or higher (e.g., AZ-104 Azure Administrator Associate, AZ-400 Azure DevOps Engineer Expert).
- Bachelor’s degree in Computer Science, Engineering, or related field.
- Strong understanding of artifact versioning, publishing practices, and dependency management.
- Background in building dashboards and observability tools to monitor deployment metrics and release health.
SUMMARY & OVERVIEW:
We are looking for a Release Engineer with a collaborative, can-do mindset who takes pride in helping teams ship reliable data products. You will design and build the automated code promotion framework for our data platforms and run the day-to-day release process. Your work will focus on packaging and promoting SQL, Python, and PySpark code, as well as ADF, Databricks, and Snowflake changes, with clear validation gates, approvals, and audit trails. You will set up monitoring, rollback paths, and hotfix flows so releases are fast, secure, and recoverable.
Responsibilities:
- Build and run automated code packaging/promotion using Azure DevOps Pipelines YAML and GitHub Actions.
- Build pull-request validation gates including code style, linting, unit tests, dependency checks, and secret scans.
- Implement approval flows, artifact versioning, release notes, and deployment audit logs.
- Package and promote data code across ADF, Databricks, Snowflake, and SQL with environment configs.
- Enable safe releases with rollback triggers, pre-deployment backups, and cherry-pick hotfix support.
- Create monitoring and alerts for promotions, track success rate, change failure rate, and time to recover.
- Standardize with reusable release templates and containerized promotion jobs.
- Use Git effectively including branching, pull-request reviews, required checks, and protected branches
- Orchestrate notifications and service integrations using Logic Apps and Azure Functions.
- Collaborate with Data Engineering and DataOps to plan releases and resolve issues quickly.
- Build observability for release health using pipeline logs, metrics, and dashboards.
- Document standards and runbooks in Confluence and manage work in Jira including release tickets and RCA.
Compensation:
$70.00 - $74.00 per hour
Exact compensation may vary based on several factors, including skills, experience, and education.
Employees in this role will enjoy a comprehensive benefits package starting on day one of employment, including options for medical, dental, and vision insurance. Eligibility to enroll in the 401(k) retirement plan begins after 90 days of employment. Additionally, employees in this role will have access to paid sick leave and other paid time off benefits as required under the applicable law of the worksite location.
