Senior Databricks Platform Practitioner

Senior Databricks Platform Practitioner This role serves as an embedded technical practitioner within one or two of the company's vertical delivery pods, operating as a player-coach who elevates the team's ability to deliver against the operating model the company data engineering leadership has defined. The role is not a traditional consulting engagement: the practitioner is expected to be a genuine member of the pod team, present in the daily work, and accountable to the same delivery cadence as the company own engineers. Core Responsibilities The practitioner participates fully in pod ceremonies including standups, sprint planning, retrospectives, and demos. They provide real-time architectural guidance and PR review feedback on pipelines, transformations, and data product work being built within the pod. Where co-development accelerates learning, they pair directly with the company's engineers on active deliverables. Where patterns are drifting from the intended operating model, they intervene in the work and correct course in context rather than through documentation or recommendation alone. A key part of the role is pattern enforcement: ensuring that ingestion, transformation, and governance work being produced within the pod reflects Databricks best practices, adheres to the medallion architecture standards the company has defined, and meets the definition of done required for Gold-layer data products to be trusted by downstream consumers. The practitioner also contributes to lightweight pattern documentation and playbook development, capturing the rationale behind key architectural decisions so that the company engineers can replicate and extend those patterns independently after the engagement. Ideal Background 5 to 8 years of hands-on data engineering experience with a strong concentration in Databricks and the broader cloud data platform ecosystem. Deep working knowledge of PySpark, Delta Lake, Databricks Workflows, and Unity Catalog is required. Experience building production-grade Bronze to Silver to Gold medallion pipelines, implementing ingestion patterns across database, file, and API source archetypes, and applying data quality controls between medallion layers. Familiarity with CI/CD practices for data pipelines, including Git-based workflows, environment promotion patterns, and orchestration observability. Prior experience working within agile pod or squad delivery models is strongly preferred. The right candidate is as comfortable pairing with a junior engineer on a specific implementation problem as they are advising a pod lead on an architectural trade-off. They lead by doing, not by presenting.