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Unlike embedded systems, some of enterprise systems are evolved over the decades. The predictability of requirements is a key to success in building reusable assets however it is very hard to predict future business context changes, which are driving factors of requirements. Thus, both functional and context variability must be managed in order to satisfy ever-changing requirements. Scalability does matter for enterprise systems in two aspects. One aspect comes from data volume. Once data become big, it is difficult to maintain performance requirements without de-normalizing database schema. Since database de-normalization is driven by non-functional properties, a model driven approach is not feasible if the model cannot express such properties. Another aspect comes from the unpredictability of future functional requirements. A functional decomposition of enterprise systems usually introduces ever-increasing complexity among systems' interactions due to cross-cutting requirements across functional systems. This paper reflects our empirical studies in data intensive large enterprise systems such as retail and telecommunication industries with industry independent application framework to separate functional and non-functional concerns. Our variability management technique is based on database schema modeling, which can be evolved incrementally in scaling an enterprise system with both data and functional aspects.