Manager, Software Engineering, Data Science

About Trust Data Science at LinkedIn
The Trust Data Science team powers the mission of creating safe trusted and professional experiences on LinkedIn through rigorous metrics experimentation and advanced data solutions. Measuring trust is inherently challenging as abuse is adversarial ground truth is noisy and outcomes are often long-tailed. We tackle these challenges using advanced statistical techniques to design and build robust actionable metrics make them experimentable while building highly reliable semantically rich data pipelines enabling data-driven decision making across the Trust organization. 

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.

About the role
We are hiring a senior engineering and data leader to lead our Trust Data Engineering Solutions team a critical and strategic pillar of Trust Data Science at LinkedIn. This team owns the data foundations platforms and user facing tools that power Trust measurement decisionmaking and AIdriven workflows for the Trust R&D organization. 

This role sits at the intersection of data engineering fullstack development and AIenabled analytics. It directly enables both human decisionmakers (data scientists analysts PMs engineers and ops) and machine consumers (analytics agents experimentation systems ML and agentic platforms) to safely reliably and accurately drive data driven decisions. 

This is a highjudgment leadership role: you will define how Trust data is produced standardized governed discovered and consumed - at scale and under realworld constraints.

Responsibilities: What you and your team will own:

Trust data foundations

• Own the endtoend strategy and evolution of Trust data foundations including:

  • Canonical Trust metrics

  • Authoritative datasets (metrics system data telemetry)

  • Measurementcritical pipelines used by Trust R&D org and external compliance reporting

• Architect and operate complex multisystem data pipelines spanning telemetry ingestion transformation ML based measurement and serving

• Set and uphold explicit SLAs across latency freshness correctness and availability balancing speed with Trustgrade reliability

Platform integration & ecosystem leadership

• Platformize Trust data by deeply integrating with:

  • Unified metrics and dimensional foundations

  • Experimentation and evaluation platforms

  • Analytics agents and GenAIenabled tooling

• Act as a technical partner and peer to trust foundations data infra ML infra and experimentation teams

Trustnative tools & data democratization

• Lead the development of Trustnative data products including:

  • Dashboards and reporting surfaces

  • Data access APIs and services to LinkedIn wide data and agentic platforms

  • Internal data tools that lower the barrier to safe correct data usage

• Democratize access to Trust data for analysts data scientists PMs Engineers and Trust Ops while maintaining appropriate guardrails.

• Enable agentbased consumption of Trust data by making datasets and metrics discoverable wellannotated and machineinterpretable

Standards governance and context

• Establish and drive adoption of standards for telemetry schema metadata and annotation across fragmented upstream systems

• Ensure Trust data carries the right context definitions assumptions limitations and lineage to support accurate retrieval and highstakes decisions.

People & org leadership

• Build lead and develop a highimpact team of data and platform engineers

• Set a strong technical and cultural bar through architecture reviews design rigor and mentorship.

• Help grow senior ICs and future leaders within the Trust Data Engineering Solutions org

Key challenges your will help tackle:

• Fragmented and inconsistent Trust telemetry across multiple upstream systems

• Complex DAG orchestration with heterogeneous SLAs and dependencies

• Measurement pipelines that combine data engineering with ML models

• Making Trust data discoverable explainable and safe for both humans and AI agents

• Scaling platforms without sacrificing metric integrity

Qualifications :

Basic Qualifications:

• 8 years of experience in data engineering platform engineering or closely related domains

• 1 year(s) of management experience or 1 year(s) of staff level engineering experience with management training

• Proven experience owning and evolving largescale data platforms not just individual pipelines

• Experience designing and operating complex multisystem data workflows

• Experience with architectural judgment and ability to reason about tradeoffs under ambiguity

• Experience building internal data tools or platforms used by diverse nonhomogeneous stakeholders

• Demonstrated people leadership: building teams setting technical direction and mentoring senior engineers

Preferred Qualifications:

• AI fluency including experience with GenAI tooling LLMassisted analytics or agentic platforms

• Fullstack development experience (APIs backend services internal UIs)

• Experience working with ML pipelines measurement models or modelintheloop systems

• Prior exposure to Trust Risk Safety Fraud Integrity or highstakes measurement domains

Additional Information :

Suggested Skills :

• AI Fluency
• Data Modelling
• Distributed Systems
• Relational Databases
• Technical Leadership
• Data Manipulation

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No

Employment Type :

Full-time