Pros
Strong exposure to enterprise-scale data projects (10TB+ pipelines) with opportunities to implement cutting-edge tools like Dagster, Snowflake, and MLflow. Collaborative teams encourage innovation in machine learning deployment (XGBoost/ARIMA/LSTM) and cloud migration initiatives. Global client projects provide valuable experience in financial analytics and inventory forecasting. Robust internal upskilling programs for AWS/Azure certifications.
Cons
Occasional resource constraints on legacy systems create technical debt challenges. High-pressure deadlines sometimes lead to extended work hours for critical deployments. Limited flexibility in project allocation across domains. Bureaucratic processes slow down tool adoption for newer technologies like generative AI/RAG systems