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Getting the best performance from the ever-increasing number of hardware platforms has been a recurring challenge for data processing systems. In recent years, the advent of data science with its increasingly numerous and complex types of analytics has made this challenge even more difficult. In practice, system designers are overwhelmed by the number of combinations and typically implement only one analysis/platform combination, leading to repeated implementation effort -- and a plethora of semi-compatible tools for data scientists. In this paper, we propose the "Collection Virtual Machine" (or CVM) -- an extensible compiler framework designed to keep the specialization process of data analytics systems tractable. It can capture at the same time the essence of a large span of low-level, hardware-specific implementation techniques as well as high-level operations of different types of analyses. At its core lies a language for defining nested, collection-oriented intermediate representations (IRs). Frontends produce programs in their IR flavors defined in that language, which get optimized through a series of rewritings (possibly changing the IR flavor multiple times) until the program is finally expressed in an IR of platform-specific operators. While reducing the overall implementation effort, this also improves the interoperability of both analyses and hardware platforms. We have used CVM successfully to build specialized backends for platforms as diverse as multi-core CPUs, RDMA clusters, and serverless computing infrastructure in the cloud and expect similar results for many more frontends and hardware platforms in the near future.