data reprocessing / recovery procedures / production incident troubleshooting
media platforms
DSPs
integrations
data warehouse design and architecture
ETL
data pipeline monitoring
data synchronization
demand-side platforms
data systems
Responsibilities
This role serves as the primary technical liaison between Customer Success and Engineering teams, ensuring accurate issue identification, high-quality escalations, and efficient resolution of data discrepancies across multiple media platforms, DSPs, and internal systems.
Act as the primary technical liaison between Customer Success and Engineering teams to expedite the resolution of complex data-related issues.
Execute the Trace protocol by performing detailed log-level investigations and data validation activities within Databricks.
Analyze and reconcile data discrepancies across multiple media platforms, DSPs, and internal systems.
Validate client configurations, integrations, and platform setups to identify data inconsistencies and operational issues.
Execute engineering-defined runbooks and procedures to trigger data reprocessing, synchronization, and recovery activities.
Perform root cause analysis of data anomalies and provide actionable findings to stakeholders.
Monitor data flows, pipelines, and synchronization processes to ensure data accuracy and completeness.
Create comprehensive and highly contextualized defect packages, including logs, analysis results, reproduction steps, and supporting evidence.
Triage and classify technical issues, ensuring accurate routing to the appropriate engineering teams or technical pods.
Maintain a high standard of escalation quality, targeting accurate defect identification and efficient engineering handoff.
Collaborate closely with Engineering, Product, and Customer Success teams to reduce Time-to-Resolution (TTR) and improve customer experience.
Develop and enhance operational troubleshooting documentation, runbooks, and knowledge base articles.
Identify recurring data quality issues and recommend process improvements, automation opportunities, and preventive measures.
Support incident investigations and post-mortem activities by providing detailed data analysis and technical insights.
Serve as a technical subject matter expert in data reconciliation, system diagnostics, and operational troubleshooting.
Ability to investigate data discrepancies, analyze logs, identify anomalies, and perform root cause analysis.
Experience with data validation, reconciliation, and troubleshooting across multiple systems or platforms.
Ability to follow technical procedures and runbooks, manage escalations, and support incident investigations.
Requirements
Strong SQL skills, with the ability to query, analyze, validate, and reconcile complex datasets.
Hands-on experience with Databricks for data investigation, validation, and troubleshooting.
Understanding of data warehousing concepts, data flows, and ETL processes.
Ability to analyze technical issues, document findings, and prepare clear, evidence-based defect reports for engineering teams.
Strong analytical and problem-solving skills, with attention to detail and a focus on data accuracy.
Effective communication and collaboration skills to work with cross-functional teams, including Engineering, Product, and Customer Success.
Good documentation skills, with the ability to develop and maintain troubleshooting guides, runbooks, and knowledge base articles.
Preferred
Experience with data warehouse design and architecture.
Familiarity with ETL processes, data pipeline monitoring, and data synchronization.
Experience with data reprocessing, recovery procedures, or production incident troubleshooting.
Exposure to media platforms, demand-side platforms (DSPs), or integrations across multiple data systems.
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