Meta

There were interesting technical problems with real business impact and access to large, well-structured datasets.

There were supportive teammates who are willing to help, review code, and share context across projects.

There was reasonable flexibility for remote work and hours, as long as deadlines and on-call responsibilities are met.

There was modern tooling for data engineering, such as cloud services, pipelines, and dashboards, that makes it easier to ship features.

There were clear expectations for performance at each level, with regular check-ins from the manager.

On-call or production support can be stressful, with incidents sometimes happening outside normal hours.

Cross-team communication is occasionally slow, which can delay dependencies and create last-minute crunch.

Promotion and compensation processes are not always transparent, making long-term growth harder to plan.

Some legacy systems are under-documented, so ramp-up on older services takes more time than expected.

Workload can spike around big launches or quarter-end deadlines, impacting work-life balance.

Invest more in cross-team alignment and documentation so dependencies and handoffs are smoother.

Continue improving transparency around promotions, leveling, and compensation bands to build trust.

Monitor on-call load and staffing to keep incidents manageable and avoid burnout on key teams.