Senior SIEM Data Engineer

State Street•Quincy, MA

3d•$120,000 - $202,500•Hybrid

About The Position

The Senior SIEM Data Engineer reports directly to the Cyber Data Engineering Manager and leads the design, onboarding, transformation, validation, and operational support of cybersecurity telemetry and enterprise log data. The role ensures reliable delivery of high-value data to cyber data platforms such as Splunk, Databricks, and other SIEM or cyber analytics platforms for security monitoring, analytics, reporting, incident response, and cyber data science use cases. The position focuses on understanding diverse enterprise data sources, designing scalable telemetry pipelines, improving log fidelity and data quality, and ensuring data is accurate, complete, searchable, governed, and fit for purpose. The engineer works with cybersecurity, infrastructure, cloud, application, and data engineering teams, establishes onboarding standards, mentors engineers, supports complex troubleshooting, and advances enterprise cyber data engineering capabilities. The role may follow a hybrid work model, with in-office presence based on team, business, and location expectations. Standard working hours are 8:00 AM to 5:00 PM local time for the employee’s designated work location. Flexibility may be needed for occasional operational support, release activities, incident resolution, escalation support, or data delivery recovery efforts.

Requirements

8+ years of experience in SIEM engineering, cybersecurity data engineering, security platform engineering, log analytics, telemetry operations, or large-scale log onboarding, with strong hands-on experience using Splunk or similar SIEM/log analytics platforms.
Strong experience with SIEM data onboarding, security telemetry pipelines, log ingestion, routing, parsing, enrichment, validation, and troubleshooting.
Strong hands-on experience with Splunk or similar SIEM/log analytics platforms.
Ability to understand and onboard telemetry from diverse enterprise data sources across cloud, endpoint, identity, network, application, infrastructure, database, SaaS, and security platforms.
Strong understanding of data quality concepts, including log fidelity, freshness, completeness, correctness, availability, schema consistency, duplicate detection, routing validation, and ingestion latency.
Strong understanding of data engineering concepts such as data layers, schema design, metadata management, transformation, enrichment, deduplication, batch/streaming ingestion, and pipeline monitoring.
Practical knowledge of SIEM taxonomy, source classification, security use case mapping, CIM/ECS alignment, schema mapping, and enterprise log onboarding standards.
Strong understanding of DevOps, DataOps, DevSecOps, CI/CD, infrastructure-as-code, automation, and production support practices.
Strong troubleshooting, documentation, communication, stakeholder management, prioritization, mentoring, and end-to-end ownership skills.
Proven experience designing, onboarding, validating, and troubleshooting security telemetry from diverse enterprise sources for threat detection, observability, incident response, reporting, cyber analytics, and data science use cases.
Experience querying, validating, and transforming data using SPL, SQL, Spark SQL, Python, PySpark, Shell, PowerShell, or similar languages.
Experience with cloud-native security telemetry and ingestion patterns across hybrid or multi-cloud environments.
Experience with production support, issue troubleshooting, incident management, change management, ticket documentation, root cause analysis, operational handoffs, vendor coordination, and continuous service improvement.
Experience mentoring engineers, leading technical initiatives, conducting design reviews, defining onboarding standards, and driving engineering best practices.
A master’s or bachelor’s degree in computer science, Cybersecurity, Information Technology, Engineering, Data Engineering, Data Analytics, Information Systems, or a related technical field; equivalent work experience may also be considered.

Nice To Haves

3+ years of experience with Cribl Stream or similar data pipeline technologies such as Fluent Bit/Fluentd, Vector, Kafka, Syslog, HEC, REST APIs, cloud-native ingestion services, or streaming platforms.
2+ years of experience with Databricks, data lakehouse platforms, large-scale analytics platforms, or security data repositories for telemetry validation, analytics, and data engineering use cases.
Relevant certifications are preferred, including Cribl certifications such as Cribl Certified Observability Engineer, Splunk certifications such as Splunk Certified Architect or Splunk Certified Consultant, or equivalent hands-on platform engineering experience.
Cloud, infrastructure, data engineering, or cybersecurity certifications are a plus.

Responsibilities

Design, build, and maintain scalable 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 or analytics use cases.
Build and optimize telemetry pipelines for parsing, filtering, masking, enrichment, normalization, event breaking, metadata tagging, and multi-destination routing.
Deliver reliable security telemetry to Splunk, 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 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.
Optimize telemetry 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.
Provide second-line-of-defense support, escalation, and root cause analysis for operational issues related to data engineering jobs, pipelines, ingestion failures, and 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 and maintain onboarding 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.