Since 2016, Antarctic sea-ice extent has undergone an abrupt regime shift, with a sequence of record low years not observed in the preceding satellite record. These recent anomalies further underline the need for improved characterization of sea‑ice and snow thickness. We primarily use radar (CryoSat-2 and Sentinel-3) and laser (ICESat-2) satellite altimetry to estimate radar and total freeboard. The difference between the two freeboard retrievals provides an estimate of snow depth. However, snow on Antarctic sea ice is very complex, affecting elevation distribution of radar backscattering targets, leading to higher uncertainties than in the Arctic. Monthly gridded datasets are produced by averaging along‑track altimetry data, but sea-ice drift can affect spatial consistency, especially when combining measurements from different satellites. To address this, we combine altimetry with satellite-derived sea-ice drift retrievals to generate drift‑aware daily freeboard, thickness, and snow-depth estimates. Within the ESA “Sea Ice Mass Balance Assessment: Southern Ocean” (SO-SIMBA) project, we advect along‑track CryoSat‑2, Sentinel‑3, and ICESat‑2 measurements over a <span>± 15</span>‑day window to preserve spatial structures, reduce temporal smearing, and improve the co‑location of radar and laser freeboards. Merging several altimeters increases the sampling density and enables an effective spatial resolution of 12.5 km for the sea-ice thickness dataset. We also track uncertainties from the raw satellite measurements to the final gridded products, combining measurement uncertainties and drift‑related propagation errors. Here we present a nearly 7-year long dataset (October 2018 to August 2025) of year-round daily updated, monthly sea‑ice thickness and snow depth along with uncertainties. Initial results show coherent and realistic spatial patterns, consistent with features visible in SAR imagery. Comparisons with independent airborne measurements, and available in-situ observations, indicate improved spatial fidelity and internal consistency compared to standard monthly gridded approaches, especially if only one sensor is used.