ETL jobs

In the following article three ETL jobs will be analysed with their main required responsibilities and expectations that incumbents for each position should met. ETL means extracting, transforming and loading the data into a data warehouse, therefore, specialists in ETL have to be accountable for proper and correct execution of these operations.

ETL developer

The person on the ETL developer job has to develop, maintain and support data warehouse in the enterprise. The main task is to conduct solid review and analysis of the objects and data models to improve data management and accelerate the access. The ETL developer needs to supervise other team members by inspecting and approving their code by means of development standards. A person suitable for this ETL job should have wide expertise on data standards and be capable of quick and correct problems diagnosis and of applying appropriate solutions.


    Detailed responsibilities of a typical ETL developer:
  • designing, constructing, testing and implementing extraction and transformation programs, processes and cycles for historical and current source data,
  • complying with data warehouse development rules of the company,
  • creating technical specification for supporting ETL processes,
  • supplementing system documentation for ETL processes,
  • solving data problems,
  • inspecting warehouse developers work,
  • cooperating with business analysts, source system experts and other teams,
  • cooperating with IT operations,
  • providing constant, immediate and professional support in a production environment.

ETL architect

The second ETL job is the ETL architect. This position requires advanced designing, developing as well as consultative skills from the incumbent. The ETL architect is a leader to the ETL designing team and delivery of large scale multi-terabyte data warehousing solutions is his/her responsibility. The main task is to lead the ETL architecture that consists of designing and planning phased implementation of the ETL data integration architecture. The ETL architecture needs to be scalable and performed correctly and precisely, therefore, the ETL architect should have well developed ability to identify and analyse the current problems and opportunities, be familiar with the business data requirements and collaborate with the ETL developers and business analysts.

    Detailed responsibilities:
  • developing and enforcing the architecture standards for the future code design,
  • creating a design document for the new data integration architecture according to the strong and precise designing principles,
  • forming the ETL process in accordance with specific requirements of the product,
  • examining and understanding of the business data requirements for creation the appropriate ETL architecture deliverables,
  • preparing data integration blueprint and installing and adjusting ETL infrastructure to support the data warehousing solutions,
  • collaborating with other ETL specialists.

Data modeler

The employee on the data modeler position is a member of the company’s architecture group and works on the data models development for data warehousing. It is expected that the person associated with this ETL job will be capable of delivering not only the accurate and reliable physical and logical models but also application’s data access framework to developers owing to maintaining, creating as well as supervising a process of modelling operations. The incumbent has to work in the OLTP and OLAP modelling space, therefore, to carry out tasks properly knowledge of creating these two kinds of physical data models is significant. Besides that, a person on this position needs to be able to provide an advice on improving the performance, reliability and scalability of applications.

    Detailed responsibilities of a data modeler:
  • creating and managing metadata models (BusinessObjects universes or Cognos framework manager packages for instance)
  • supervising data modelling activity by technology suppliers,
  • developing and inspecting ODS data model of the company,
  • developing and supervising data dictionary of the company,
  • business and technical knowledge about source systems, data warehouse and end user reports
  • understanding business objectives and the full lifecycle of the projects.