Senior Staff Software Engineer, GRACE 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 dont just protect our members customers and employeeswe 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 nearreal-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 securityensuring 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 organizations mission.

ᐧ Contribute to engineering innovations that fuel LinkedIns vision and mission.

Qualifications :

Basic Qualifications

ᐧ Bachelors Degree in a quantitative discipline: Computer Science Statistics Operations Research Informatics Engineering Applied Mathematics Economics etc.

ᐧ 7 years of relevant industry experience.

ᐧ Experience with SQL/Relational databases.

ᐧ Background in at least one programming language (e.g. R Python Java Scala PHP JavaScript Python Java and Scala).

Preferred Qualifications

ᐧ BS and 11 years of relevant work experience MS and 9 years of relevant work experience or Ph.D. and 7 years of relevant work/academia experience working with large amounts of data.

ᐧ 7 years in technical leadership roles for large-scale platforms.

ᐧ Deep experience mapping technical systems to compliance/risk frameworks.

ᐧ Proven success launching and scaling analytics/compliance systems.

ᐧ Experience designing and governing data models schemas metadata standards and data quality controls for structured and unstructured security/compliance data.
ᐧ Knowledge of data lineage lifecycle management and privacy/regulatory frameworks (e.g. GDPR CCPA HIPAA SOX).
ᐧ Experience developing analytics and ML pipelines using tools such as Spark Databricks Snowflake or equivalent distributed compute frameworks.
ᐧ Experience authoring and orchestrating data pipelines with workflow tools such as Airflow Prefect Flyte or dbt.
ᐧ Familiarity with anomaly detection control drift analysis predictive modeling and quantitative risk techniques (e.g. Monte Carlo simulation Bayesian inference VaR).
ᐧ Experience using BI and reporting platforms (e.g. Tableau Power BI Looker) to produce executive-ready dashboards and audit artifacts.
ᐧ Knowledge of programming languages such as Python Scala R or TypeScript and experience with ML libraries (e.g. scikit-learn TensorFlow PyTorch).
ᐧ Familiarity with integration patterns across systems of record transformation and insight including data contracts encryption authentication and secrets management.
ᐧ Experience with cloud-native compute and storage platforms (e.g. AWS Azure GCP) data lakes (e.g. S3 ADLS) and Git-based source control workflows.
ᐧ Knowledge of CI/CD MLOps and infrastructure-as-code concepts for secure reliable model and platform deployment.
ᐧ Experience with GRC compliance or security automation platforms (e.g. ServiceNow Archer OneTrust) and shift-left security practices within the SDLC.
ᐧ Familiarity with designing high-volume high-reliability data foundations in large distributed software environments.
ᐧ Experience creating data visualizations and communicating technical concepts clearly to engineering InfoSec audit and business stakeholders.
ᐧ Demonstrated ability to mentor engineers and collaborate cross-functionally with legal security and platform teams.
ᐧ Background working in regulated or highly audited environments (e.g. finance healthcare government SaaS).

Suggested Skills

ᐧExperience building data science or machine learning platforms.

ᐧExperience writing RESTful / GRPC APIs with modern frameworks.

ᐧWeb App Development

ᐧTechnical Leadership

ᐧHands-on experience in building data pipelines to transform unstructured data into structured formats with emerging knowledge of leveraging Large Language Models (LLMs) to enhance data processing and transformation workflows.

You Will Benefit from Our Culture We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels.

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $181000 to $297000. Actual compensation packages are based on a wide array of factors unique to each candidate including but not limited to skill set years & depth of experience certifications and specific office location. This may differ in other locations due to cost of labor considerations. The total compensation package for this position may also include an annual performance bonus stock and benefits. For additional information visit: LinkedIn Benefits

Additional Information :

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No

Employment Type :

Full-time