Senior Data Engineer - Internal Data Platform & Analytics

About this job

Glean is looking for a Senior Data Engineer to join its internal data platform and analytics team in Bangalore, India. Glean is a Work AI platform that helps organizations work smarter through advanced enterprise search, an AI Assistant, and scalable AI agents on a secure platform. In this role, you will operate as an individual contributor focusing entirely on internal-facing data infrastructure rather than customer-facing implementations. Your core mission involves building reliable, governed, and cost-efficient data platforms, working across data ingestion, transformation, modeling, quality, access governance, and platform reliability. A normal day in this role involves building batch and API-based data ingestion pipelines, optimizing BigQuery infrastructure for performance and cost, implementing data access controls, and partnering with internal teams like Data Science, Finance, and Marketing to understand their data requirements. You will also participate in design and code reviews, improve pipeline observability, and ensure high standards of data quality through rigorous testing and CI workflows.

Responsibilities

• Build and maintain reliable batch and API-based data ingestion pipelines.
• Improve data quality through testing, continuous integration 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 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.

Requirements

• Minimum experience of 7 to 10 years overall, including at least 7 years of data engineering experience.
• Strong data engineering fundamentals and experience building production data systems.
• High AI proficiency through habitual, high-value use of LLMs with sound judgment and rigorous validation.
• 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.

Good to have

• 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.

Who should apply

This role suits an experienced data engineer with a strong background in building production-grade data systems and internal data platforms. Candidates must possess 7 to 10 years of overall experience with at least 7 years dedicated to data engineering, alongside high AI proficiency and familiarity with tools like BigQuery, Python, SQL, and DBT. Applicants should be comfortable collaborating across IST and US PST time zones and able to work in a hybrid setup from the Bangalore office. This position is not suitable for freshers.

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