The answer to this question depends on the company where you are applying for a position in the data management department. In big companies that conduct trials in several therapeutic areas or programs it is not efficient to have all people from the data management department doing everything. It would be a waste of time to assign different tasks across all data managers working on a project. So it seems reasonable to create sub-teams that are trained and specialized on certain tasks and thus to create more specialized job descriptions like project data manager, data programmer, data coordinator or coding specialist.
However, having such specialists will reduce flexibility. A good example from my own experience is from the programming of validation procedures. An edit check was programmed by the data programmer and tested “successfully” by the data manager in the test environment. However, in the real data it caused many queries due to a simple “OR-AND-issue” in the SQL. The data manager had restricted rights in the system and was not able to do the update. The office of the programming group was in a different time zone. So it took several hours for an update that actually only took a few minutes.
In smaller data management groups it does not make sense to define several specialized job descriptions with restricted rights. The team needs to be able to react to changes in project plans and timelines immediately without getting into trouble, e.g. cancellation or postponement of a trial. So everybody needs to have a good knowledge of all data management processes from the beginning (almost always protocol review) to the end (data management report after database lock). A common example is a sudden need to have validation checks programmed and tested on short notice due to an upcoming interim transfer where most of the data managers are involved at the same time.
However, also in a small group that consists of all-rounders almost entirely, some tasks should be handled by specialists. I am sure that you will hear about the common CDISC standards for data collection (CDASH) and submitting the data to regulatory authorities (SDTM). Those standards are really complex and changes are implemented on an ongoing basis. A general understanding is essential for all people using it. However, some of the decisions, for instance which domain should be used for a new assessment when defining the metadata, have an influence beyond that initial project. Thus, it is reasonable to have a kind of a “standards committee” controlling the definitions across projects; even in small teams.
I hope that I could give you a rough overview on different approaches how data management teams may be setup and when they might be useful. For sure the two I described here are not the only ones. The structure of a data management group depends on additional factors like the systems (eCRF, database etc). Hence the degree of specialization and the kind of job descriptions changes not only between companies but possibly within a company over time as well.
If you are a data manager or want to work in the field of clinical trial data management then choosing a company with the team structure that best suits your personal preferences will have a big impact on what type of data manager you will be in the future. Do you want to be a generalist with a more varied set of tasks or do you want to be a specialist with expert knowledge within a subset of tasks? In the end it is your decision what you prefer. A good advice could be: just try and find out! Either way, it can be an exciting job.
Check our career page for open positions. Our data management team has been growing continuously.