Intermediate Data Engineer

Overview

Build, run, and maintain the pipelines and infrastructure that bring data from enterprise applications, internal systems, and GitLab's SaaS platform into the data platform, powering company reporting, data science, and AI self-service. This is an AI-first role.

What you'll do

• Build, extend, and maintain ELT pipelines from enterprise applications such as Salesforce, Zuora, Zendesk, and Marketo, internal systems, and the GitLab SaaS platform.
• Integrate data through application programming interfaces (APIs) and event sources such as Snowplow.
• Manage and maintain the infrastructure behind the platform, including SaaS tools such as Snowflake, Fivetran, and Monte Carlo, and self-managed services such as Airflow and Kubernetes.
• Write maintainable, well-tested, performant Python and SQL code that meets internal standards, and review GitLab team members' merge requests.
• Own day-to-day platform operations: triage and resolve failed pipelines, infrastructure issues, and GitLab team member requests.
• Use monitoring, service-level agreements (SLAs), data contracts, query and warehouse tuning, and data quality checks to keep the platform reliable and cost-efficient.
• Partner with Analytics Engineers, Data Analysts, Data Scientists, Data Governance, and business partners so data lands in the shape they need for downstream use.
• Use AI coding agents such as Claude Code, OpenCode, GitLab Duo, or similar tools to speed up development, debugging, testing, and documentation without compromising review rigor.
• Apply role-based access control (RBAC) and masking policies to make governed data available to AI tools and agents.
• Handle data subject to Sarbanes-Oxley (SOX) controls, material nonpublic information (MNPI), and personal data safely.
• Ship medium-sized features independently in small, iterative merge requests, and document architecture and runbooks in the handbook.

What you'll need

• Professional experience shipping production data pipelines in Python.
• Deep, hands-on SQL skills and experience with a cloud data warehouse.
• Hands-on experience with ELT or ETL pipelines at scale, including incremental loads, schema changes, backfills, and API ingestion.
• Experience with orchestration tools such as Airflow, familiarity with dbt, and solid data modeling fundamentals, including Kimball dimensional modeling.
• Experience running and troubleshooting data infrastructure, with comfort using Git-based workflows, CI/CD, and code review.
• Hands-on experience using AI coding tools in your daily work, with an understanding of where they help and where they fall short.
• Clear written communication, the ability to use GitLab, and comfort working asynchronously in a fully remote organization in line with GitLab's values.

Nice to have

• Experience with Snowflake.
• Docker, Kubernetes, Google Cloud Platform (GCP), or Terraform.
• SaaS application APIs such as Salesforce, Zuora, or Zendesk.
• Data observability tooling.
• Snowflake administration.

Details

• Location: Bangalore, India.
• Fully remote organization; all roles are remote, subject to country-specific location-based eligibility requirements.
• Reports to the Director, Data Platform.
• Employment type: full-time employees.