Senior Data Engineer - Internal Data Platform & Analytics

• Minimum experience: 7–10 years overall, including at least 7 years of data engineering experience. • Strong Data engineering fundamentals and experience building production data systems. • An exceptionally high AI proficiency through habitual, high-value use of LLMs; sound judgment about when and how to apply them; rigorous validation and workflow improvement • Experience with SQL and Python, or comparable programming languages. • Experience with a cloud data warehouse, preferably BigQuery or a similar platform. • Experience with data transformation frameworks such as DBT, including testing and deployment workflows. • In Depth Understanding of Columnar File systems like parquet, Hudi Or Iceberg. • Understanding of dimensional modeling, data contracts, lineage, and data quality practices. • Experience designing or operating APIs, batch pipelines, or event-driven ingestion systems. • Ability to communicate technical trade-offs clearly and work effectively with internal stakeholders. • Ownership mindset: you can take a problem from discovery through implementation, rollout, and operational follow-through. • Ability to maintain a productive collaboration between IST and US PST time zones • Experience with BigQuery governance, IAM/RBAC, policy tags, masking, streaming systems or cost controls. • Experience building reusable data platform frameworks rather than one-off pipelines. • Familiarity with semantic layers, metric stores, or systems that make trusted data consumable by AI and analytics tools. • Experience with data observability, orchestration, CI/CD, or infrastructure-as-code. • Experience working in a fast-growing company where requirements and priorities evolve quickly.

About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. About the Role: We are looking for an individual-contributor Data Engineer to help build and operate Glean’s internal data platform. This is an internal-facing data platform role focused on analytics engineering for Glean’s internal teams and not customer-facing implementation. The role will focus on reliable, governed, cost-efficient data infrastructure and analytics foundations used by internal teams across the company. You will work across data ingestion, transformation, modeling, quality, access governance, and platform reliability. This role is hands-on and well suited to an engineer who enjoys turning ambiguous data problems into durable systems and clear operating standards. This is an in-person role based in Bangalore, India, with regular office presence expected. You will: • Build and maintain reliable batch and API-based data ingestion pipelines. • Improve data quality through testing, continuous integration (CI) checks, ownership metadata, and clear layer boundaries. • Operate and improve BigQuery data infrastructure with an emphasis on performance and cost efficiency. • Implement data access controls, governance workflows, and safe self-serve access patterns. • Improve pipeline observability, failure classification, incident triage, and recovery processes. • Partner with Data Science, Business Intelligence, Finance, Sales Operations, Marketing, Security, Reliability Engineering, and other internal teams to understand data needs and deliver reusable platform capabilities. • Participate in design reviews, code reviews, documentation, and operational support for the data platform. About you: • Minimum experience: 7–10 years overall, including at least 7 years of data engineering experience. • Strong Data engineering fundamentals and experience building production data systems. • An exceptionally high AI proficiency through habitual, high-value use of LLMs; sound judgment about when and how to apply them; rigorous validation and workflow improvement • Experience with SQL and Python, or comparable programming languages. • Experience with a cloud data warehouse, preferably BigQuery or a similar platform. • Experience with data transformation frameworks such as DBT, including testing and deployment workflows. • In Depth Understanding of Columnar File systems like parquet, Hudi Or Iceberg. • Understanding of dimensional modeling, data contracts, lineage, and data quality practices. • Experience designing or operating APIs, batch pipelines, or event-driven ingestion systems. • Ability to communicate technical trade-offs clearly and work effectively with internal stakeholders. • Ownership mindset: you can take a problem from discovery through implementation, rollout, and operational follow-through. • Ability to maintain a productive collaboration between IST and US PST time zones • Experience with BigQuery governance, IAM/RBAC, policy tags, masking, streaming systems or cost controls. • Experience building reusable data platform frameworks rather than one-off pipelines. • Familiarity with semantic layers, metric stores, or systems that make trusted data consumable by AI and analytics tools. • Experience with data observability, orchestration, CI/CD, or infrastructure-as-code. • Experience working in a fast-growing company where requirements and priorities evolve quickly. Location: • This role is hybrid (4 days a week in our Bangalore office) Compensation & Benefits: Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. We’re committed to building and sustaining a diverse, inclusive workplace. We strive to attract and retain people with a wide range of backgrounds, experiences, and perspectives, and we do not discriminate on the basis of gender, ethnicity, sexual orientation