Senior Data Engineer - Internal Data Platform & Analytics

Gleanwork
Bangalore, India
On-siteCareer-pivot friendly

Who this role is best for

Strong fit for data engineers with deep AI proficiency and cloud data warehouse experience who thrive in hybrid work arrangements and can manage cross-functional collaboration.

Best fit for

  • Candidates with 7–10 years of data engineering experience and a track record in building reusable data platform frameworks
    — “Experience building reusable data platform frameworks rather than one-off pipelines.”
  • Individuals who can take ambiguous data problems and deliver durable systems with clear standards
    — “you can take a problem from discovery through implementation, rollout, and operational follow-through.”
  • Engineers with strong experience in BigQuery governance and data quality practices
    — “Experience with BigQuery governance, IAM/RBAC, policy tags, masking, streaming systems or cost controls.”

Things to consider

  • The role requires regular in-office presence in Bangalore, India, with 4 days per week expected
    — “This role is hybrid (4 days a week in our Bangalore office)”
  • Collaboration across multiple internal teams is a core part of the role, requiring cross-functional communication
    — “Partner with Data Science, Business Intelligence, Finance, Sales Operations, Marketing, Security, Reliability Engineering, and other internal teams”

How to stand out

  • Highlight experience with data observability and CI/CD for data pipelines in your resume and interviews
    — “Experience with data observability, orchestration, CI/CD, or infrastructure-as-code.”
  • Showcase your ability to implement data access governance and secure self-serve patterns
    — “Implement data access controls, governance workflows, and safe self-serve access patterns.”
  • Emphasize your background in dimensional modeling and data contracts in your application materials
    — “Understanding of dimensional modeling, data contracts, lineage, and data quality practices.”
  • Demonstrate your experience with columnar file systems like Parquet, Hudi, or Iceberg
    — “In Depth Understanding of Columnar File systems like parquet, Hudi Or Iceberg.”
  • Demonstrate your ability to work across IST and US PST time zones effectively
    — “Ability to maintain a productive collaboration between IST and US PST time zones”
Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • building reliable data pipelines
  • operating BigQuery data infrastructure
  • implementing data access controls
Typical background
data engineeringdata infrastructureBigQuery

Skills & requirements

Required

Data IngestionData QualityBigQueryData Access Controls

Preferred

Agentic CapabilitiesLLM Choice

About the role

Original posting from Gleanwork via Greenhouse

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, religion, civil or family status, age, disability, or race.

#LI-HYBRID 

AI-First Mindset at Glean:

At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required.

Global Data Privacy Notice for Job Candidates and Applicants:

Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy. By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement.

By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement, and I agree to the terms.  

Source: Gleanwork careers (Greenhouse)

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