Senior Staff Software Engineer, Risk and Compliance Infrastructure & Data Science

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.

Trust is our foundation. At LinkedIn, we build secure, compliant infrastructure with integrity woven into every layer. By embedding security, governance, and regulatory alignment into our development lifecycle and business, we don’t just protect our members, customers, and employees—we set the standard for trusted technology and operations at scale. GRACE is a team leading entity-wide compliance and risk management programs. GRACE stands for Governance, Risk, Automation, Compliance and Engineering.

Our commitment to our customers and members is engineered into our culture of security and compliance through these foundational pillars:

• Proactive Governance & Engineering Alignment

• Scaled Lifecycle & Integrated Controls

• Assured Ecosystem & Quantified Risk Management

LinkedIn is looking for a technical lead to provide architectural and technical leadership across GRACE infrastructure platforms, including engineering repositories, datalakes and analytics platforms, and the GRACE system of record. This role emphasizes data science, quantitative risk analysis, and automation at scale to deliver audit-ready systems, predictive insights, and risk quantification. The role requires deep expertise in data modeling, machine learning, and advanced analytics to ensure secure, scalable, and integrated compliance platforms.

Responsibilities

ᐧ Define and drive architecture and roadmap for enterprise GRC and security platforms, ensuring alignment with organizational security, audit, and compliance objectives.

ᐧ Author technical specifications for self-service compliance reporting, transformation of security metadata into audit-ready artifacts, and CI/CD integration for engineering controls.

ᐧ Design and oversee real-time, near–real-time, and batch data pipelines that support live dashboarding, anomaly detection, predictive modeling, and executive reporting.

ᐧ Mentor engineering and data science teams by conducting pipeline/code reviews, promoting scalable patterns, and enabling maintainable deployment of quantitative models.

ᐧ Govern development of certifiable reporting and audit systems, policy-as-code and docs-as-code engines, and analytics platforms supporting predictive and anomaly-based risk intelligence.

ᐧ Ensure secure, efficient integration and data flow across systems of record, systems of transformation, and systems of insight.

ᐧ Implement tooling and automation that streamline compliance workflows, enable self-service analytics, and improve quantitative risk measurement.

ᐧ Design and enforce data models, metadata standards, lineage tracking, lifecycle management processes, and data integrity controls for structured and unstructured security-relevant data.

ᐧ Lead the implementation of advanced analytics capabilities leveraging statistical and ML techniques to quantify control effectiveness and risk posture.

ᐧ Architect secure, performant integration strategies using APIs, ETL/ELT mechanisms, and workflow orchestrators; develop and manage data contracts and integration security protocols.

ᐧ Champion platform performance, scalability, and security—ensuring confidentiality, integrity, and availability for computationally intensive risk workloads.

ᐧ Partner with engineering teams to onboard new compliance and risk programs; enable other risk domains to leverage shared infrastructure and platform capabilities.

ᐧ Collaborate with internal data platform teams to influence in-house tooling that supports insight generation, workflow automation, and data-driven risk decisions.

ᐧ Establish and socialize engineering and data ecosystem best practices across technical and non-technical teams, promoting standardization and design consistency.

ᐧ Serve as an escalation point for complex technical, data quality, and model deployment issues; provide guidance on resolution paths.

ᐧ Contribute to engineering innovations that strengthen security posture and advance the organization’s mission.

ᐧ Contribute to engineering innovations that fuel LinkedIn’s vision and mission.