Manager, Software Engineering, Data Science at LinkedIn

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

17 Jun, 26

Salary

0.0

Posted On

19 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Platform Engineering, Data Science, Full-Stack Development, AI-Enabled Analytics, Data Governance, Metrics Design, Experimentation, Data Pipelines, System Integration, API Development, Technical Leadership, Mentorship, Risk Management, Safety

Industry

Software Development

Description

Company Description LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed. Join us to transform the way the world works. Job Description 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 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 large‑scale data platforms, not just individual pipelines Experience designing and operating complex, multi‑system data workflows Experience with architectural judgment and ability to reason about trade‑offs under ambiguity Experience building internal data tools or platforms used by diverse, non‑homogeneous stakeholders Demonstrated people leadership: building teams, setting technical direction, and mentoring senior engineers Preferred Qualifications: AI fluency, including experience with GenAI tooling, LLM‑assisted analytics, or agentic platforms Full‑stack development experience (APIs, backend services, internal UIs) Experience working with ML pipelines, measurement models, or model‑in‑the‑loop systems Prior exposure to Trust, Risk, Safety, Fraud, Integrity, or high‑stakes measurement domains Additional Information Suggested Skills : AI Fluency Data Modelling Distributed Systems Relational Databases Technical Leadership Data Manipulation India Disability Policy LinkedIn is an equal employment opportunity employer offering opportunities to all job seekers, including individuals with disabilities. For more information on our equal opportunity policy, please visit https://legal.linkedin.com/content/dam/legal/Policy_India_EqualOppPWD_9-12-2023.pdf Global Data Privacy Notice for Job Candidates Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal. Workplace Type: Hybrid Career Track & Grade: MR3/9 Department: Engineering

Responsibilities

This role involves owning the end-to-end strategy and evolution of Trust data foundations, including canonical metrics, authoritative datasets, and measurement-critical pipelines, while also leading the development of Trust-native data products like dashboards and APIs. The leader will also be responsible for establishing and driving adoption of standards for telemetry, schema, and metadata across upstream systems to ensure data context and integrity.