L1 Support - Azure databrick

Dear Candidate,

We are Hiring for Azure Databrick for Hyderabad Location.

Please help me with below detail along with you update resume.

Note Screenselected resumes are Scheduled

Name
Contact Number
Email
Current Location
Preferred Location
Total Experience
Relevant Experience
Current Organisation
CurrentCTC
Expected
Notice Period
Timing
Date

Experience:

5+ Years

Location:

[Onsite/Hybrid/Remote]

Employment Type:

Full-time

Role Overview
We’re seeking a detail-oriented  L1 Data Engineering (Databricks) Engineer  to provide  first-line support  for data pipelines and jobs running on  Databricks . You will be responsible for  monitoring ,  incident response ,  job reruns ,  SLA adherence , and  operational hygiene . The ideal candidate has solid exposure to  PySpark ,  Delta Lake ,  Azure Data Lake Storage , and  Databricks Workflows/Jobs , with a strong focus on  stability  and  service continuity .

Key Responsibilities
Monitor & Support Pipelines:
Monitor  Databricks Jobs/Workflows , ADX/ADF pipeline runs, and streaming sources.
Proactively detect failures, lag, and SLA breaches; perform  first-line triage .
Incident Management:
Acknowledge incidents, classify severity (SEV levels), and follow  ITIL-based process .
Execute  runbooks , perform  safe reruns , and handle  partial reprocessing .
Escalate to L2/L3 with detailed  incident documentation  (logs, job IDs, inputs/outputs).
Operational Tasks:
Validate data availability and quality at key checkpoints ( bronze/silver/gold  layers).
Manage  ad hoc fixes  (e.g., small Spark config changes, partition reruns).
Maintain  metadata ,  service accounts , and  token rotations  as per SOPs.
Access & Governance:
Basic administration in  Databricks : cluster start/stop, job scheduling checks, permissions requests, workspace hygiene.
Work with  Unity Catalog  permissions and  Key Vault  integrations under guidance.
Documentation & Reporting:
Update  runbooks ,  Known Error Database (KEDB) , and  SOPs .
Publish daily/weekly  ops reports , SLA metrics, and post-incident summaries.

Collaboration:

Coordinate with Data Engineers, Platform Engineers, Security, and Product Teams.
Participate in  release readiness  and  operational acceptance  for new pipelines.

Required

Skills & Qualifications
5+ years  in data operations/support (L1/L1.5), preferably on  Databricks + Azure .
Hands-on with:
Databricks Jobs/Workflows ,  Clusters ,  Repos ,  Delta Lake  basics.
Azure Data Lake Storage (ADLS) ,  Azure Data Factory (ADF)  monitoring.
PySpark  basics: reading/writing Delta/Parquet, partitioning, checkpoints.
Git  (Git Hub/Azure Repos) for config, notebooks, and runbook versioning.
Strong grasp of  observability :
Reading  Spark UI ,  job logs ,  driver/executor logs .

Experience with  Log Analytics ,  Azure Monitor ,  App Insights  (preferred).
ITIL/Service Management  familiarity:
Incident, Change, Problem, Knowledge.
Scripting  (Bash/Power Shell/Python) for small automation tasks.
Excellent  communication ,  documentation , and  shift handover  discipline.

Nice-to-Have
Exposure to  Structured Streaming ,  Kafka/Event Hub  monitoring.
Basic  SQL  for data validation and health checks.
Understanding of  Unity Catalog  data governance, lineage, and entitlement workflows.

Experience with  Secrets/Key Vault ,  Managed Identity , and  RBAC .

Experience with  CI/CD  for Databricks (Git Hub Actions/Azure Dev Ops) for deployments.

Certifications (Preferred)
Databricks Certified Data Engineer Associate
Microsoft Certified:
Azure Data Engineer Associate (DP-203)
ITIL Foundation  (v3/v4)

Key Performance Indicators (KPIs)
SLA Adherence : % of jobs meeting SLA; mean time to acknowledge (MTTA).
Incident Metrics :
Mean time to resolve (MTTR), incident reopen rate.
Operational Hygiene :
Runbook completeness, KEDB updates, shift handover quality.
Quality Metrics :
Error rates, number of successful reruns without escalation.
Proactive Monitoring :
Number of issues prevented via early detection/alerts.
Change Readiness :
Zero-defect deployments from ops perspective.

Tools & Ecosystem
Databricks  (Jobs, Clusters, Repos, Workflows, Unity Catalog)
Azure : ADLS, ADF, Key Vault, Event Hub, Log Analytics, Monitor
Version Control :
Git Hub / Azure Repos
Ticketi…