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Scicom Infrastructure ServicesCore Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in monitoring and supporting Databricks environments, including ETL and ELT data pipelines, while ensuring operational efficiency and data quality. Proficient in troubleshooting, incident management, and maintaining technical documentation to support production workloads.
Highest-signal resume keywords
Databricks Environment SupportETL/ELT Data Pipeline MonitoringApache Spark and PySparkCloud Platform Experience (Azure, AWS, Google Cloud)Incident and Change Management
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Monitor the health, availability, performance, and capacity of Databricks workspaces, jobs, pipelines, clusters, SQL warehouses, and related cloud services
- Provide day-to-day operational support for production Databricks environments
- Monitor and troubleshoot ETL, ELT, streaming, API, and file-based data pipelines
- Restart, rerun, recover, reprocess, backfill, reconcile, and remediate failed workflows and data loads
- Review driver and executor logs, Spark UI metrics, cluster event logs, audit logs, and application telemetry to identify root causes
- Support Databricks Workflows, Lakeflow, Delta Live Tables, Auto Loader, Structured Streaming, and batch-processing workloads
- Maintain cluster, serverless compute, instance pool, cluster policy, SQL warehouse, and environment configurations
- Coordinate platform upgrades, runtime and library updates, deployments, releases, changes, rollbacks, and post-deployment monitoring
- Serve as first- or second-level support for production incidents; triage, coordinate response, provide status updates, escalate, and conduct root-cause analyses
- Maintain incident, problem, service-request, change, deployment, and operational records
- Develop and maintain dashboards, alerts, operational reports, health indicators, SLIs, and SLOs
- Monitor job success, latency, processing duration, utilization, freshness, quality, and compute consumption
- Validate data contracts, schemas, record counts, control totals, data quality, lineage, audit evidence, and governance requirements
- Support identity, access, entitlement, Unity Catalog, secrets, security, privacy, backup, recovery, retention, archival, and disaster-recovery activities
- Analyze resource consumption, job duration, query performance, storage, and workload patterns; recommend cost and performance optimizations
- Create and maintain operational procedures, troubleshooting guides, escalation matrices, runbooks, inventories, configuration documentation, and recovery requirements
- Identify automation and continuous-improvement opportunities and participate in operational-readiness reviews
Requirements
What you’ll need- Bachelor’s degree in computer science, information technology, data engineering, information systems, or a related field, or equivalent professional experience
- At least four years of experience in data operations, production support, data engineering, cloud operations, DevOps, platform support, or a related technical role
- At least two years of hands-on experience supporting Databricks environments or production Databricks workloads
- Experience supporting production ETL, ELT, batch, or streaming data pipelines
- Working knowledge of Databricks, Apache Spark, PySpark or Python, SQL, Delta Lake, Databricks Workflows or Jobs, cloud object storage, data pipeline monitoring, log analysis, and troubleshooting
- Experience investigating failed jobs, delayed pipelines, schema issues, data discrepancies, and performance problems
- Familiarity with medallion architecture and Bronze, Silver, and Gold data layers
- Experience with at least one major cloud platform: Microsoft Azure, AWS, or Google Cloud
- Experience using ticketing and service-management tools such as ServiceNow, Jira, or a comparable platform
- Experience with monitoring, alerting, logging, or observability tools
- Understanding of incident, problem, change, and release-management processes
- Ability to write and maintain technical procedures and operational documentation
- Strong analytical, organizational, communication, and troubleshooting skills
- Ability to work effectively with engineers, business analysts, architects, security teams, and nontechnical stakeholders
- Ability to support multiple production priorities in a structured and timely manner
- Preferred: Databricks Certified Data Engineer Associate or Professional; Databricks Certified Associate Developer for Apache Spark; Microsoft Azure, AWS, or Google Cloud certification
- Preferred: Experience with Unity Catalog, Delta Live Tables, Lakeflow, Auto Loader, Structured Streaming, Databricks SQL, cloud services, CI/CD tools, observability platforms, APIs, Kafka, Event Hubs, Kinesis, Airflow, dbt, Snowflake, Power BI, data contracts, schema registries, metadata management, data lineage, data-quality frameworks, OCDS, procurement, contracting, financial or public-sector data, regulated or high-security environments, SLAs and 24-hour support, disaster recovery, backup, archival, and operational-resilience testing
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
DatabricksApache SparkPySparkSQLDelta LakeData Pipeline MonitoringLog AnalysisCloud Object StorageCI/CD ToolsData Quality Frameworks
Soft Skills
Analytical SkillsOrganizational SkillsCommunication SkillsTroubleshooting Skills
Tools & Technologies
ServiceNowJiraMonitoring ToolsObservability Platforms
Certifications & Qualifications
Databricks Certified Data Engineer AssociateDatabricks Certified Professional Data EngineerMicrosoft Azure CertificationAWS CertificationGoogle Cloud Certification
Industry Keywords
Data OperationsProduction SupportCloud OperationsMedallion ArchitectureData LineageDisaster RecoveryOperational Resilience