As you may know, the metric tensor is a bilinear 2-form. It accepts two vectors from vector space $V$ and gives back a real number in $\mathbb R$. It is linear in both arguments, hence 'bilinear'. The metric tensor is interpreted as a linear operator in the sense that it maps one of its arguments (either one; doesn't matter because it's symmetric) to a dual vector in $V^*$. This dual vector is interpreted as a functional on $V$ (the traditional definition of the dual space of $V$), which acts on the second vector to give a scalar value. So $g$ is a linear map from $V$ to $V^*$. When you write $g$ as a matrix and operate on a column vector $v$, transpose the resulting vector to make it a row vector and you have the dual vector $v^*$.
From a general point of view, the metric tensor is a rank 2 tensor, specifically a rank $(0,2)$ tensor. In general, a rank $(n,m)$ tensor is a multilinear functional which acts on an ordered collection of vectors in $V$ and dual vectors in the dual space $V^*$. For a vector space $V$ over a field $\mathbb F$ (usually $\mathbb R$ or $\mathbb C$), a tensor $T$ is a multilinear map of the form
$$ T : V^m \times V^{*n} \rightarrow \mathbb F .$$
Rank $(0,2)$ tensors over the real numbers, like $g_{\mu \nu}$,
$$ g : V \times V \rightarrow \mathbb R$$
are particularly interesting as they often appear in mathematics and physics. This is because they define inner products. The inner product between two vectors $\begin{pmatrix}a_1\\a_2\end{pmatrix}$ and $\begin{pmatrix}b_1\\b_2\end{pmatrix}$ in an inner product space $V$ is
$$\begin{pmatrix}a_1\\a_2\end{pmatrix} \cdot \begin{pmatrix}b_1\\b_2\end{pmatrix} = \begin{pmatrix}a_1&a_2\end{pmatrix} \begin{pmatrix}A_{11}&A_{12}\\A_{21}&A_{22}\end{pmatrix} \begin{pmatrix}b_1\\b_2\end{pmatrix}$$
where $\mathbf A$ forms a symmetric positive-definite matrix (symmetric with positive real eigenvalues). By convention we normally write vectors in $V$ in an orthonormal basis, which is a basis that diagonalises $\mathbf A$ to the identity matrix, and so we usually omit $\mathbf A$ entirely when taking inner products because of this orthonormal choice of basis:
$$\begin{pmatrix}a_1\\a_2\end{pmatrix} \cdot \begin{pmatrix}b_1\\b_2\end{pmatrix} = \begin{pmatrix}a_1&a_2\end{pmatrix} \begin{pmatrix}b_1\\b_2\end{pmatrix}$$
when the vectors are written in an orthonormal basis.
These inner products $\mathbf A$ are basically the same thing as metric tensors $g$. Two terms for one concept. Of course in pseudoriemannian geometry, $\mathbf A$/$g$ need not be positive-definite. It is clear how $\mathbf A$ should be interpreted as a linear operator though, right? It maps the vector $\mathbf b$ to its dual vector $\mathbf b^*$ like so:
$$\mathbf b^* (\mathbf a) = \mathbf a \cdot \mathbf b \tag{definition of dual vector space $V^*$}$$
$$\begin{align}\mathbf a \cdot \mathbf b &= \begin{pmatrix}a_1&a_2\end{pmatrix} \begin{pmatrix}A_{11}&A_{12}\\A_{21}&A_{22}\end{pmatrix} \begin{pmatrix}b_1\\b_2\end{pmatrix} \\ &= \left[ \begin{pmatrix}A_{11}&A_{12}\\A_{21}&A_{22}\end{pmatrix} \begin{pmatrix}b_1\\b_2\end{pmatrix} \right]^{\mathrm T} \begin{pmatrix}a_1\\a_2\end{pmatrix} \\ &\Rightarrow \quad \mathbf b^* = \left[ \begin{pmatrix}A_{11}&A_{12}\\A_{21}&A_{22}\end{pmatrix} \begin{pmatrix}b_1\\b_2\end{pmatrix} \right]^{\mathrm T} \end{align}.$$
Having to take the transpose makes this a little confusing, but it should be clear that $\mathbf A$ defines a dual vector $\mathbf b^*$ for each vector $\mathbf b$.
The concept of a metric tensor is basically the same thing, but with a different notation. I could not say earlier that $\mathbf A$ maps a vector to its dual, but that it defines such a map. This is because I had to use the transpose operation. Matrices are a notation designed to express vectors $V$ and linear operators $M : V \rightarrow V$, and the notation is not flexible enough to express a linear map $V \rightarrow V^*$. The notation used for expressing metric tensors (upper/lower index notation; tensor notation; not sure if it has a better name) is more flexible. The metric is denoted $g$, and by writing it with two lower indices as $g_{\mu \nu}$ we are designating it as a rank $(0,2)$ tensor that maps $V \times V \rightarrow \mathbb R$. By giving $g_{\mu \nu}$ just one argument and leaving the other empty, we are left with a map $V \rightarrow \mathbb R$, which is the same thing as a dual vector in $V^*$. We write vectors by their components, $x^\mu$, and then $g$ defines a linear map $g : V \rightarrow V^*$ like so:
$$g : \mathbf x \mapsto \mathbf x^*, \quad x_\mu = \sum_{\nu} g_{\mu \nu} x^{\nu}.$$
The notation $x^\mu$ expresses the components of the vector $\mathbf x$ in the chosen basis of $V$, and $x_\mu$ expresses the components of the dual vector $\mathbf x^*$ in the dual basis of $V$, i.e. the corresponding basis in $V^*$. The notation is frequently heavily abused for brevity, so you may see expressions like
$$g : x^\mu \rightarrow g_{\mu \nu} x^\nu \tag{implied summation over $\nu$}$$
to mean the same thing as I said above.
You may notice the similarity with matrix multiplication:
$$(b^*)_\mu = \sum_{\nu} A_{\mu \nu} b_\nu.$$
When $g$ is expressed as a matrix as in your question, it simply maps the components $x^\mu$ to the components of its dual vector, $x_\mu$. It very much is a linear map, $g : V \rightarrow V^*$ and all the associated tools of linear analysis may be applied.
