In this role, you will lead the development of advanced security data engineering and analytics capabilities that help organizations identify, prioritize, and reduce cyber risk. You will integrate and enrich data from multiple security, cloud, application, identity, and governance platforms to transform complex information into actionable intelligence. Combining hands-on engineering expertise with technical leadership, you will build scalable data pipelines, trusted metrics, and reusable data products that strengthen security decision-making. You will collaborate with security specialists, data engineering teams, risk leaders, and business stakeholders to improve visibility into vulnerabilities, control effectiveness, and remediation outcomes. You will also help establish engineering standards, mentor technical colleagues, and support the evolution of automation and AI-enabled security capabilities. This opportunity offers a meaningful scope of impact within a collaborative, globally connected environment focused on innovation, measurable risk reduction, and continuous improvement.
Lead security data engineering initiatives: Provide technical direction for complex security data projects and cross-functional initiatives, overseeing solution design, implementation, delivery, and ongoing operational ownership.
Integrate security data sources: Build scalable data pipelines, applications, APIs, and automation to ingest information from security, cloud, application, endpoint, identity, vulnerability, governance, and risk management platforms.
Transform data into actionable intelligence: Deduplicate, correlate, normalize, and enrich security findings with asset ownership, business context, control information, and risk indicators to improve prioritization and remediation.
Develop governed data products: Design and deliver reusable security data products through enterprise data platforms, leveraging Snowflake and established data engineering practices to make security information reliable, accessible, and reusable.
Optimize data architecture and performance: Develop and maintain Snowflake data models, SQL transformations, queries, and compute patterns while ensuring data quality, performance, access controls, traceability, and cost efficiency.
Establish data governance practices: Collaborate with enterprise data, governance, and security teams to implement sustainable practices for data ingestion, transformation, role-based access, lineage, metadata, quality assurance, and lifecycle management.
Define security performance metrics: Develop and operationalize key performance indicators (KPIs), key risk indicators (KRIs), control-effectiveness measures, thresholds, targets, and calculation methodologies aligned with security strategy and risk objectives.
Ensure metric consistency and accountability: Establish documented definitions, authoritative data sources, clear ownership, validation processes, refresh schedules, version control, and traceability between metrics, risks, controls, and remediation activities.
Deliver meaningful reporting and dashboards: Build technology-agnostic dashboards, scorecards, reporting capabilities, and reusable services that allow stakeholders to move from executive-level indicators to the underlying assets, controls, findings, and remediation actions.
Translate analytics into recommendations: Identify emerging trends, systemic risks, concentrated exposures, control gaps, and remediation opportunities, communicating clear recommendations to leadership and technical teams.
Support automation and AI innovation: Build strong data foundations for security automation and future AI-enabled capabilities, including integrations based on the Model Context Protocol (MCP).
Mentor and develop technical talent: Guide engineers through technical design, peer reviews, training, knowledge sharing, and the adoption of consistent engineering standards.
Evaluate technologies and improve efficiency: Assess emerging security, data, and analytics technologies, recommending solutions that maximize business value, reduce manual work, and minimize unnecessary vendor dependency.
Communicate technical direction: Clearly explain architecture decisions, technical trade-offs, delivery progress, risks, and recommendations to engineers, business stakeholders, control owners, and senior leaders.
Drive measurable security outcomes: Ensure that data products and reporting improve risk prioritization, control visibility, remediation effectiveness, security debt measurement, and the ability to quantify the value of security initiatives.