Data Operations Engineer

GLG is the world’s leading B2B platform connecting professionals with expertise. We connect thousands of clients to the largest global network of subject-matter experts across every sector. We bring the power of insight to every great professional decision.

Our Product Engineering group is responsible for GLG’s technology platforms that connect GLG’s clients with the insights needed to make important business decisions. We’re looking for a curious Data Operations Engineer with 2–3 years’ experience to join our Data Operations team and keep the data that powers GLG flowing reliably. You’ll pair with other engineers, analysts, and stakeholders to build, operate, and troubleshoot the pipelines and integrations that move data across our systems.

Specific responsibilities include (but are not limited to):

• Build, run, and maintain data pipelines, imports, and integrations that feed GLG’s platforms and reporting.


• Write and optimize SQL to move, transform, and validate data across sources.


• Monitor scheduled jobs, triage failures, and resolve data-quality and data-accuracy issues.


• Collaborate with cross-functional teams to turn business needs into reliable data deliverables.


• Write clean, maintainable, and efficient code and queries.


• Contribute to runbooks, documentation, and operational best practices.


• Stay curious about new tools and approaches — we’ll train you on our stack as you grow.

An ideal candidate will have the following:

• 2–3 years of experience in a data operations, data engineering, or similar data-focused role.


• Strong SQL and a solid understanding of relational databases.


• Proficiency in a scripting language (Python preferred) for data manipulation and automation.


• Solid grasp of data fundamentals — modeling, transformation, data quality.


• An inquisitive, generalist mindset — eager to learn systems end-to-end rather than staying in one niche.


• Experience using Claude, Copilot, Opencode and other AI toolsets.


• Solid problem-solving skills and the ability to thrive in an iterative, collaborative environment.

Preferred (or willing to train):

• Cloud data warehouses (e.g., Snowflake).


• Workflow orchestration (e.g., Airflow).


• Search/indexing platforms (e.g., OpenSearch).


• AWS cloud platform, CI/CD pipelines, and DevOps practices.