Who we are looking for

We are lookingfor aSenior SIEM Data Engineerreporting directly to theCyber Data Engineering Manager. You will lead the design, onboarding, transformation, validation, and operational support of cybersecurity telemetry and enterprise log data used for security monitoring, analytics, reporting, incident response, and cyber data science use cases.

This role is focused on understanding diverse enterprise data sources, designing scalable security telemetry pipelines, improving log fidelity and data quality, and ensuring reliable delivery of high-value data into cyber data platforms such as Splunk, Databricks, and other SIEM or cyber analytics platforms. You will work closely with cybersecurity, infrastructure, cloud, application, and data engineering teams to ensure security telemetry isaccurate, complete, searchable, governed, and fit for purpose. As a senior engineer, you will also help define onboarding standards, mentor engineers, drive operational maturity, support complex troubleshooting, and contribute to the evolution of enterprise cyber data engineering capabilities.

What you willbe responsible for

AsSeniorSIEM Data Engineeryou will:

• Design, build, andmaintainscalable SIEM and security telemetry pipelines across hybrid and multi-cloud environments.

• Lead onboarding of security telemetry from applications, infrastructure, endpoints, identity platforms, network devices, cloud services, SaaS tools, databases, and security products.

• Analyze source log formats and define onboarding requirements, expected fields, metadata, routing needs, retention considerations, and downstream SIEM/analytics use cases.

• Build andoptimizetelemetry pipelines for parsing, filtering, masking, enrichment, normalization, event breaking, metadata tagging, and multi-destination routing.

• Deliver reliable security telemetry toSplunk, Databricks, and other SIEM or cyber analytics platforms.

• Design and support Databricks data engineering patterns across raw, enriched, curated, and analytics-ready data layers.

• Validate data quality across cyber data platforms for freshness, completeness, correctness, availability, timestamp accuracy, schema consistency, source attribution, routing accuracy, and latency.

• Establish SIEM onboarding standards for source classification, source types, index routing, taxonomy alignment, metadata tagging, schema expectations, CIM/ECS alignment, and data quality controls.

• Optimizetelemetry pipelines to reduce noise, control ingestion cost, improve performance, and preserve high-value security data for detection and investigation.

• Lead troubleshooting of complex ingestion and data flow issues across sources, collectors, pipelines, SIEM platforms, Databricks tables, APIs, cloud storage, and streaming platforms.

• Providesecond-line-of-defense support, escalation, and root cause analysisfor operational issues related to data engineering jobs, pipelines, ingestion failures, and issues leading to loss of data delivery to cyber data platforms.

• Automate deployment, monitoring, alerting, validation, pipeline testing, repeatable onboarding, and operational support using CI/CD and infrastructure-as-code practices.

• Partner with Detection Engineering, Security Operations, Cyber Data Science, Observability, Cloud, Infrastructure, Application, Governance, Risk, and Platform teams to ensure telemetry supports business and security use cases.

• Create andmaintainonboarding standards, data flow diagrams, field mappings, transformation logic, runbooks, troubleshooting procedures, operational handoffs, and engineering documentation.

• Mentor engineers and promote best practices for security telemetry onboarding, data quality, automation, reliability, operational excellence, and secure engineering.

What we value

These skills will help you succeed in this role

• Strong experience with SIEM data onboarding, security telemetry pipelines, log ingestion, routing, parsing, enrichment, validation, and troubleshooting.

• Strong hands-on experience withSplunkor similar SIEM/log analytics platforms.

• Hands-on experience withCribl Streamor similar data pipeline technologies for routing, filtering, parsing, enrichment, transformation, event breaking, replay, and multi-destination delivery.

• Ability to understand and onboard telemetry from diverse enterprise data sources across cloud, endpoint, identity, network, application, infrastructure, database, SaaS, and security platforms.