[Hiring] Databricks Data Operations Specialist @Scicom Infrastructure Services

Role Description

Scicom Infrastructure Services is seeking a Databricks Data Operations Specialist to support the operational stability, reliability, performance, and security of a large-scale Databricks data platform. This role will work as part of a multidisciplinary Databricks team that includes data engineers, architects, business analysts, governance specialists, and technical managers. The Data Operations Specialist will be responsible for:

• Monitoring production data pipelines
• Responding to incidents
• Troubleshooting platform and data-processing issues
• Coordinating deployments
• Ensuring data products meet established availability, quality, and service-level requirements

The ideal candidate will have hands-on experience operating Databricks environments and supporting production data workloads using:

• Apache Spark
• Delta Lake
• SQL
• Python
• Cloud infrastructure
• Monitoring tools
• Automated deployment practices

Qualifications

• 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
• 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

Requirements

• Databricks Certified Data Engineer Associate or Databricks Certified Data Engineer Professional
• Databricks Certified Associate Developer for Apache Spark
• Microsoft Azure, AWS, or Google Cloud certification
• Experience with Unity Catalog, Delta Live Tables, Lakeflow, Auto Loader, Structured Streaming, or Databricks SQL
• Experience with Azure Data Factory, Azure Data Lake Storage, Azure Monitor, Log Analytics, AWS Glue, Amazon S3, CloudWatch, or comparable cloud services
• Experience with Terraform, Git, Azure DevOps, GitHub Actions, Jenkins, or other CI/CD tools
• Experience with Splunk, Datadog, Grafana, Prometheus, or similar observability platforms
• Experience supporting APIs, Kafka, Event Hubs, Kinesis, Airflow, dbt, Snowflake, or Power BI
• Knowledge of data contracts, schema registries, metadata management, data lineage, and data-quality frameworks
• Familiarity with OCDS, procurement data, contracting data, financial data, or public-sector data standards
• Experience supporting government, regulated, or high-security environments
• Experience operating systems with formal service-level agreements and 24-hour support requirements
• Experience with disaster recovery, backup, archival, and operational-resilience testing

Core Competencies

• Strong operational ownership and attention to detail
• Ability to remain calm and organized during production incidents
• Clear written and verbal communication
• Methodical troubleshooting and root-cause analysis
• Ability to distinguish symptoms from underlying technical causes
• Strong documentation and process-discipline skills
• Ability to prioritize incidents based on business and technical impact
• Collaborative approach to working with engineering and business teams
• Commitment to automation, reliability, security, and continuous improvement
• Willingness to take ownership of issues through resolution