Job Role: Sr Databricks Platform Engineer – Azure Cloud & Data Operations"
About the Opportunit
yOur client is undergoing a large-scale enterprise modernization initiative focused on Azure-native infrastructure, platform engineering, automation, and modernization of data-intensive application ecosystems
.As part of this transformation, multiple calculation-heavy and operationally critical applications are being integrated into a growing Databricks-centered enterprise data platform spanning
- :Azure cloud service
- sAKS and containerized platform
- sGitHub and CI/CD ecosystem
- senterprise automation and analytics environment
sWe are seeking a Senior Databricks Platform Engineer to support architecture, operational engineering, platform enablement, and modernization activities across this evolving environment
.This role blends
- :Databricks platform expertis
- ecloud infrastructure awarenes
- sDevOps-aligned engineerin
- goperational platform suppor
tThe ideal candidate understands how Databricks workloads, APIs, pipelines, storage layers, and operational processes integrate within broader enterprise cloud ecosystems
.Role Overvie
wThe Senior Databricks Platform Engineer will support the design, operational enablement, modernization, and ongoing support of enterprise Databricks platforms and associated data processing environments
.This engineer will work closely with
- :cloud platform team
- sDevOps organization
- sinfrastructure engineering team
- sapplication modernization group
- sdata and analytics stakeholder
sThe role requires strong awareness of how Databricks modernization activities impact
- :existing CI/CD pipeline
- sautomation framework
- soperational governanc
- eenterprise cloud platform standard
sKey Responsibilitie
sDatabricks Platform Engineering & Operation
- sSupport architecture and operational engineering across enterprise Databricks environment
- sDesign and maintain scalable Lakehouse, Delta Lake, and Unity Catalog solution
- sSupport administration of
- :Databricks Workspace
- sJob
- sNotebook
- sCluster
- sOptimize Spark workloads, cluster configurations, and platform performanc
- eSupport batch and streaming data processing workload
- sAssist with multi-environment platform management across Dev, QA, and Productio
nPlatform Integration & DevOps Alignmen
- tSupport integration of Databricks platforms with Azure-native ecosystems and enterprise application
- sWork across integrations involving
- :API
- sAzure Storage Account
- sData Lake
- sreporting and analytics platform
- sCollaborate with platform and DevOps teams to align Databricks activities with existing CI/CD and automation standard
- sSupport deployment workflows utilizing
- :GitHu
- bAzure DevOp
- sInfrastructure as Code framework
- sHelp ensure modernization activities do not disrupt existing automated deployment pipelines and operational processe
sGovernance, Reliability & Platform Suppor
- tSupport governance, access management, monitoring, logging, and operational security practice
- sAssist with implementation of
- :RBA
- Clineag
- eencryptio
- nplatform governance control
- sParticipate in troubleshooting, operational support, and root cause analysis activitie
- sContribute to documentation, operational standards, and platform best practice
sRequired Qualification
- s6+ years of experience within cloud, platform engineering, data engineering, or enterprise analytics ecosystem
- s3+ years of hands-on experience supporting enterprise Databricks environment
- sStrong experience with
- :Delta Lak
- eUnity Catalo
- gSpark workload
- scluster operation
- sDatabricks Workspace
- sExperience with Azure cloud services and cloud-native platform concept
- sFamiliarity with
- :Azure DevOp
- sGitHub workflow
- sCI/CD pipeline concept
- sdeployment automatio
- nExperience supporting APIs, distributed application integrations, and enterprise data platform
- sStrong troubleshooting and operational support capabilitie
- sStrong communication and stakeholder collaboration skill
sPreferred Qualification
- sExperience with
- :PySpar
- kPytho
- nSQ
- LSpark SQ
- LScal
- aFamiliarity with
- :AKS/Kubernete
- sTerrafor
- mBice
- pInfrastructure as Code practice
- sExperience with
- :Azure Data Lak
- eEvent Hu
- bstreaming architecture
- senterprise analytics environment
- sFamiliarity with monitoring and observability platforms such as
- :Azure Monito
- rDatado
- gExposure to enterprise modernization and cloud migration initiative
s