• Designs and builds the architecture of both front-end and back-end for novel data applications aligned to open-ended or complex business problems/opportunities.
• Develop and build methodologies, platforms, and tooling that support experimentation, causal inference, measurement, and diagnostics for complex product and business questions
• Designs, builds, and customizes reusable tools, frameworks, dashboards, APIs, and integrations that make data applications and engineering solutions easier to adopt, operate, and extend across teams.
• Conceptualizes, defines, and socializes foundational metrics to set organizational goals and scale with AI to democratize data-driven decision-making.
• Leverages LinkedIn’s AI-enabled data stack and AI tools to accelerate discovery, design, development, testing, documentation, maintenance, and iteration of data products, applications, and pipelines.
• Uses domain knowledge to evaluate design tradeoffs, identify risks, and communicate technical plans, progress, dependencies, and recommendations to cross-functional partners so data applications, pipelines, and tooling remain useful, reliable, and aligned to business needs.
• Identifies gaps in existing data applications, tools, workflows, datasets, and pipelines, then proposes and implements improvements that reduce manual work, improve reliability, and increase reuse across projects.
• Applies data science and software engineering best practices across the lifecycle of data applications and data engineering solutions and participates in code, data model, pipeline, and design reviews, providing constructive feedback and technical guidance to peers that improves maintainability, scalability, data quality, and user experience.
• Support, monitor and continuously improve operational excellence practices for data applications and platforms, including reliability, observability, incident response, scalability, privacy, and production readiness.