Senior Data Engineer

Gloat is the AI-native platform powering the era of Agentic HR. Powered by Loomra, our context engine is purpose-built for the workforce. Gloat lets you build any Agentic HR agent that operates in the flow of work across Slack, Microsoft Teams, Copilot, and Google Chat. Talent Redeployment, internal mobility, skills intelligence, workforce redesign, and whatever comes next. Trusted by leading global enterprises, Gloat serves over 1.5 million employees across 30+ Fortune 500 companies, unlocking more than 4.8 million strategic work hours. We help organizations harness AI to unlock agility, productivity, and growth, empowering them to achieve true ROI and drive exponential productivity. About the Role We are seeking a highly skilled and analytical Senior Data Engineer to join our Data team. In this role, you will design and implement robust data pipelines, while uniquely bridging the gap between engineering and analytics by actively analyzing data to extract actionable insights. You will play a crucial part in architecting our data foundations to support everything from business intelligence to advanced machine learning and agentic AI pipelines. As a Senior Data Engineer, you will collaborate closely with engineering teams, product managers, and stakeholders across the organization. You will not only build the infrastructure utilizing modern data stack tools but also act as a data analyst when needed, ensuring our systems are fully equipped to operate within and support a cutting-edge agentic AI environment.

Requirements: Must-Have: • 5+ years of proven experience in a Data Engineering role, with a strong background in data architecture. • Exceptional proficiency in SQL and Python for data manipulation, scripting, and pipeline automation. • Deep hands-on experience with modern data orchestration and transformation tools, specifically Airflow and dbt. • Extensive experience managing and optimizing cloud data platforms such as BigQuery / Databricks / Snowflake. • Demonstrated experience in data analysis, with the ability to act as a Data Analyst to query data, build reports, and extract actionable insights. • Practical experience designing or supporting data infrastructure for an agentic environment or AI/LLM-driven applications. • Strong attention to detail, analytical mindset, and excellent communication skills. • Experience of one or more of these technologies: Kafka, Kubernetes, ArgoCD, Terraform, Debezium. • Understanding of data modeling principles: dimensional modeling, fact/dimension tables, slowly changing dimensions • Experience with Git workflows: branching, PRs, code reviews, and CI/CD for data pipelines. • Ownership mindset: ability to debug production issues, drive projects to completion independently Nice-to-Have: • Experience with BI tools (e.g., Looker, Tableau, Power BI) for advanced dashboarding. • Experience working with graph databases or NoSQL databases. • Experience with Python backend APIs (FastAPI/Flask) that serve aggregated analytics data to dashboards