Bangalore · Remote

Intermediate Data Engineer

Full-timePosted 2 Nov

Salary: Not disclosed
Eligible batches: 2024 · 2025 · 2026
Location: BangaloreRemote

About the role

GitLab is looking for an Intermediate Data Engineer to design, build, and operate ELT pipelines that feed data from enterprise applications, internal services, and GitLab’s SaaS platform into a modern data warehouse. The position is fully remote and reports to the Director of Data Platform. You will ensure data is reliable, secure, and cost‑effective while supporting reporting, analytics, data‑science, and AI‑driven use cases.

Responsibilities

Develop and maintain ELT pipelines using Python and SQL to ingest data from sources such as Salesforce, Zuora, Zendesk, Marketo, Snowplow and internal APIs.
Manage the underlying data platform infrastructure, including Snowflake, Fivetran, Monte Carlo, Airflow and Kubernetes.
Write clean, well‑tested code, perform code reviews, and follow GitLab’s CI/CD workflow.
Own day‑to‑day operations: monitor pipelines, resolve failures, tune queries, and enforce SLAs.
Collaborate with analytics engineers, data analysts, data scientists and governance teams to deliver data in the shape they need.
Leverage AI coding assistants (Claude Code, OpenCode, GitLab Duo, etc.) to accelerate development while maintaining review rigor.

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Prepare for the interview

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SQL interview questions40 questions · ≈ 19 min to revise

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Eligibility

Professional experience building production‑grade data pipelines in Python.
Strong SQL expertise and hands‑on experience with a cloud data warehouse, preferably Snowflake.
Experience with ELT/ETL at scale, including incremental loads, schema evolution and API ingestion.
Familiarity with orchestration tools like Airflow and data‑modeling concepts such as Kimball dimensional modeling.
Comfort with Git‑based workflows, CI/CD pipelines and asynchronous remote collaboration.

Skills

Python programmingSQL and SnowflakeAirflow orchestrationDbt for transformationsGit, CI/CD and code reviewETL/ELT design patternsData modeling fundamentals