You can interpret the PERWT value for this record as the individual representing 67 other persons. I do not recommend expanding the dataset as you describe; instead you should leverage weight commands (in either Stata or R) to weight to your analyses and estimate standard errors (I am linking to resources about applying weights, clustering and standard errors as well as generating standard errors using replicate weights). By expanding your dataset as you describe you will get accurate counts, but won’t estimate the correct variance around those point estimates. The weight commands account for the uncertainty of survey data (e.g., where a single record represents 67 other persons); by expanding your dataset to instead include 67 records, your data are more like a census (e.g., where there isn’t this uncertainty because your data includes the entire population).
Additionally, while I understand that you included an example as a reference point only, I want to include a caution about small sample sizes as well as some information about occupation and industry.
Small Sample Sizes
Your example is a very targeted subgroup; I only see 26 women in the 2019 1-year ACS PUMS data that are women working as financial clerks (as per the OCC variable) who are working in the state of Illinois; this is across all income groups. There is no bright line rule regarding how small of a sample is too small, but you want to avoid making population-level inferences from too small of a small sample size. For example, I would be hesitant to make statements about the income distribution of women working in finance in the entire state of Illinois from these 26 cases, and would not want to subdivide by income bracket. You might augment your unweighted case counts by collapsing related occupations together, or pooling together multiple years of data (e.g., using the multi-year files; if you pool multiple single year files, note that the weights will total to the sum of the population for each year of data you are combining, so you should divide the weights by the number of years).
Occupation & Industry
Occupation reports the type of work a person does whereas industry is the type of activity at a person’s place of work. There are multiple ways you might define “women working in finance” as outlined in your example. For example, the occupation code 5165 identifies “Other financial clerks” (note that this does not include financial managers, financial and investment analysts, personal finance advisors, tellers, payroll and timekeeping clerks, billing and posting clerks, or bill and account collectors to name a few). Industry codes beginning 6870-6992 identify people of all occupations who work in “Finance and Insurance” (you can further subdivide into more targeted industries within this group). The thing to note with industry codes is that they may include persons whose occupations may fall outside of your occupation of interest (e.g., janitorial staff), but who work in this industry. Some combination of occupation and industry may be useful as you consider how to define groups like “women working in finance.”
If you are pooling multiple years of data, it is important to note that beginning in 2018 there are new codes for occupation and industry that affect the comparability of some codes over time. IPUMS has harmonized occupation (OCC1990, OCC2010) and industry (IND1990) variables over time to address these (and previous) changes in the underlying coding schemes for occupation and industry.