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# Scrum Master with Data Migration

- **Company:** KRG Technologies
- **Location:** Juno Beach, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Airflow, JIRA, Microsoft Azure, Business Intelligence Development, Information Engineering, Extract Transform Load (ETL), Scrum Methodology, Power BI, Tableau (Software), Snowflake, Functional Dependencies, Looker Analytics, Databricks
- **Published:** September 15, 2026
- **Apply:** https://www.dice.com/job-detail/946958a9-819c-46cc-b08a-a6c72dc45251

## About the Role

* 3+ years as a Scrum Master/Agile Coach, ideally with technical teams
 * Strong grasp of Scrum, Kanban, and hybrid frameworks
 * Familiarity with data concepts (ETL/ELT, pipelines, data quality, BI cycles)
 * Excellent facilitation, communication, and conflict-resolution skills
 * Proficiency with agile tooling (Jira, Azure DevOps, etc.)

Preferred

 * CSM, PSM, or SAFe Agilist certification
 * Familiarity with dbt, Airflow, Snowflake, Databricks, Looker/Tableau/Power BI
 * Experience in scaled agile environments (SAFe, LeSS)

## Description

We're looking for an experienced Scrum Master to embed within our Data team - data engineers, analytics engineers, data scientists, and BI developers - driving agile delivery of pipelines, models, and data products. Ideal for someone who understands both agile facilitation and the unique rhythms of data work (long-running pipelines, data quality gates, cross-functional dependencies).

Roles & Responsibilities

 * Facilitate Scrum ceremonies (planning, stand-ups, backlog refinement, review, retros) for one or more data squads
 * Partner with the Data Product Owner to maintain a healthy backlog of pipeline, infrastructure, and analytics work
 * Identify and remove blockers, including cross-team dependencies with platform, product, and business stakeholders
 * Track sprint metrics (velocity, burndown, cycle time, incident rates) and report trends to leadership
 * Coach the team on agile best practices and continuously improve ways of working
 * Manage dependencies between data engineering, analytics, and downstream consumers
 * Support release coordination for pipelines/models, tracking data quality and SLAs
 * Foster a collaborative, psychologically safe team culture
 * Manage data-specific risks - schema changes, upstream instability, governance checkpoints


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