SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Glean is a Work AI platform that helps enterprises work smarter with AI. The company has evolved from enterprise search into a full-scale Work AI ecosystem, powering intelligent search, AI assistants, and scalable AI agents on a secure, open platform. Glean serves 1,000+ employees across 25+ countries and is recognized by Fast Company, CNBC, Bloomberg, Forbes, and Gartner as a leader in agentic AI.
This is an individual-contributor Senior Data Engineer role focused on building and operating Glean's internal data platform. The role is internal-facing, supporting analytics engineering for Glean's internal teams rather than customer-facing implementation. You will work on reliable, governed, cost-efficient data infrastructure and analytics foundations used across the company.
Key responsibilities include:
- Build and maintain reliable batch and API-based data ingestion pipelines
- Improve data quality through testing, CI checks, ownership metadata, and clear layer boundaries
- Operate and improve BigQuery data infrastructure with 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, and Reliability Engineering teams to understand data needs and deliver reusable platform capabilities
- Participate in design reviews, code reviews, documentation, and operational support for the data platform
This is a hands-on role well-suited to an engineer who enjoys turning ambiguous data problems into durable systems and clear operating standards. The role requires collaboration across IST and US PST time zones. The position is hybrid, requiring 4 days per week in the Bangalore office.
REQUIREMENTS:
- Minimum 7–10 years overall experience, including at least 7 years of data engineering experience
- Strong data engineering fundamentals and experience building production data systems
- 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 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: ability to take a problem from discovery through implementation, rollout, and operational follow-through
- Ability to maintain productive collaboration between IST and US PST time zones
Preferred qualifications:
- 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
