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, decision‑making, and AI‑driven workflows for the Trust R&D organization.
This role sits at the intersection of data engineering, full‑stack development, and AI‑enabled analytics. It directly enables both human decision‑makers (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 high‑judgment leadership role: you will define how Trust data is produced, standardized, governed, discovered, and consumed - at scale and under real‑world constraints.
Responsibilities: What you and your team will own:
Trust data foundations
Own the end‑to‑end strategy and evolution of Trust data foundations, including:
Canonical Trust metrics
Authoritative datasets (metrics, system data, telemetry)
Measurement‑critical pipelines used by Trust R&D org and external compliance reporting
Architect and operate complex, multi‑system 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 Trust‑grade reliability
Platform integration & ecosystem leadership
Platformize Trust data by deeply integrating with:
Unified metrics and dimensional foundations
Experimentation and evaluation platforms
Analytics agents and GenAI‑enabled tooling
Act as a technical partner and peer to trust foundations, data infra, ML infra and experimentation teams
Trust‑native tools & data democratization
Lead the development of Trust‑native 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 agent‑based consumption of Trust data by making datasets and metrics discoverable, well‑annotated, and machine‑interpretable
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 high‑stakes decisions.
People & org leadership
Build, lead, and develop a high‑impact 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
