Data Engineer
Znane również jako: Analytics Engineer · ETL Developer · Big Data Engineer
Builds and maintains data pipelines and storage so data from source systems becomes reliably, timely and repeatably available for analysis.
The data engineer builds the infrastructure that analysts and models rely on and keeps those flows running daily. Unlike the data analyst, this role does not interpret outcomes, and unlike the data architect it actually builds the chosen structure. Selection gets stuck on a list of platforms, while the real criterion is engineering discipline: version control, tests and monitoring on data flows, because a pipeline that fails silently supplies wrong figures to the whole organisation for a long time.
Kompetencje, które należy oceniać
Oto konstrukty behawioralne i kompetencyjne zawarte w tym profilu. Przy każdym z nich podano, który kwestionariusz hrmforce służy do jego pomiaru.
| Kompetencje, które należy oceniać | Poziom docelowy | hrmforce narzędzie | Metoda oceny | Kotwica behawioralna na poziomie docelowym |
|---|---|---|---|---|
| Numerical reasoning Knockout | N4 | Ability Scan | Cognitive ability test | Combines two data sources, calculates the development over time and explains which calculation step leads to which result. |
| Curiosity and exploring Kompetencje (50-ramowe): Learning Ability | N3 | Big Fifty, 15PF, Motivation | Personality questionnaire | Gathers information on new methods outside the own team and brings findings into the work meeting unprompted. |
| Dealing with ambiguity Kompetencje (50-ramowe): Flexibility | N3 | Big Fifty, Mental Resilience (4C), Competency Check | Situational Judgement Test | Formulates explicit assumptions when information is missing, proceeds on that basis and revises the approach once new data arrives. |
Krótki profil
Dziesięć najważniejszych umiejętności w tym profilu. Pełny profil zawierający wszystkie umiejętności, punkty odniesienia behawioralne i metody oceny znajduje się w pliku do pobrania.
| T | Umiejętności | Poziom docelowy | Waga | Knockout | Metoda oceny |
|---|---|---|---|---|---|
| V | Data modelling | N5 | 5 | nie | Work sample test |
| V | Data management and storage | N5 | 5 | tak | Work sample test |
| A | Numerical reasoning | N4 | 5 | tak | Cognitive ability test |
| V | Data analysis | N4 | 5 | tak | Work sample test |
| V | Programming | N4 | 5 | tak | Work sample test |
| V | API integration | N4 | 5 | nie | Work sample test |
| V | Critical thinking and source evaluation | N4 | 4 | nie | Work sample test |
| V | Assessing data quality | N4 | 4 | tak | Work sample test |
| T | Version control and collaboration workflow | N4 | 4 | nie | Work sample test |
| V | Using cloud platforms | N4 | 4 | nie | Knowledge test |
Zobacz wszystkie 26 umiejętności w pełnym profilu
Narzędzia oceny dla tego stanowiska
Work sample test
Representative work sample under standardized conditions
Knowledge test (client-specific)
Professional knowledge test, customized for each client
Competency Check
Behavioral-Level Competency Assessment
Ability Scan
Aptitude Test: Verbal, Numerical, and Abstract Reasoning Skills
Portfolio
Evaluation of prior work against fixed criteria
Structured interview
Behavioral interview with a fixed structure
Big Fifty
Personality questionnaire, 150 items, 25 factors
15PF
Personality Profile, a shorter alternative to the Big Fifty
Motivation
Drivers and sources of motivation
360 Feedback
Multiple assessments based on behavioral anchors
Mental Resilience (4C)
Mental Resilience According to the 4C Model
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