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

Responsibilities:

Design, build, and maintain highly scalable ELT/ETL data pipelines.
Architect and manage modern cloud data warehousing solutions.
Develop, maintain, and monitor Python services responsible for robust data collection and ingestion.
Perform hands-on data analysis to interpret complex datasets, identify trends, and deliver business insights, acting in a dual capacity as a Data Analyst.
Develop and optimize data infrastructure specifically designed to support autonomous agentic workflows and LLM integrations.
Collaborate with engineers and analysts to troubleshoot data issues, enforce quality SLAs, and define data requirements.
Document data architecture, flow, and analytics standards for internal team alignment.
Build and maintain dashboards and reports to communicate analytical findings and data health to the organization.
Maintain Kafka consumer applications that process high-volume event streams in real-time, ensuring reliable ingestion into cloud databases.