Because companies depend on their data engineers to build and activate the data pipelines that power analytics, the data engineer career path has now become one of the fastest-growing tech occupations.
Nonetheless, the intense demand doesn’t make the data engineer career path easy to get. Having a resume that can impress a hiring manager can get you a job. Your data engineer resume further works best by illustrating the blending of a well-defined career path and a data-driven resume outline.
Every data engineer must have set skills that qualify them for the job, as well as an outstanding performance that singles them above other job applicants.
A hiring manager spends less than five minutes reviewing a resume; it is, however, advised that your resume highlights your highest achievement rather than all your experiences.
A resume is your first point of contact with a company. Ensure you have a resume tailored to the specific job application. Resist the urge to always use a particular resume copy for all kinds of jobs, as this will not help you in your job search.
You can also check the job ad qualification requirement and see if you can fit some of the required skills into your data engineer resume. Many leading organisations scan resumes using applicant tracking systems. An ATS will scan a CV for an exact match of the skill or experience specified by the hiring manager. The more abilities you list on your CV, the more likely you are to get interviewed by a hiring manager.
Hiring managers also evaluate a data engineer’s resume for its technical knowledge and demonstrable outcomes. Be sure to explain every programming language, operating system, and database solutions you have used. Also, explain what those skills have helped you achieve in your data engineer resume.
Skills your data engineer resume must possess.
A data engineer is required to be knowledgeable in big data frameworks, databases, and the creation of data infrastructure. It is also crucial that they have hands-on exposure to the industry’s working tools, such as HPCC, Storm, Cloudera, SPSS, SAS, Excel, R, Python and Docker.
Here are examples of important skills a data engineer resume should have:
Tools for data transformation
Because big data exists in its raw form, they are left unused until it is processed. Big data are translated to a digestible format for it to be processed.
Tools used to process such big data are Hevo Data, Matillion, Talend, Pentaho Data Integration, InfoSphere DataStage, and many other available solution tools.
Tools for data ingestion
Data ingestion is the process of transporting data from one or more sources to a destination for evaluation. Data ingestion gets more difficult as the amount and formats of data expand. The process necessitates the knowledge of data ingestion tools and APIs. These tools and APIs prioritise data sources, evaluate them, and dispatch data to guarantee a successful ingestion process.
Some of the popular APIs are Apache Kafka, Apache Storm, Apache Flume, Apache Sqoop and Wavefront.
Database applications
Storing, organising, and managing massive amounts of data is vital for the data engineering job role.
Structure query language (SQL) and NoSQL-based databases are the two most prevalent forms of databases. While SQL-based databases like MySQL and PL/SQL store structured data, NoSQL technologies, on the hand, stores vast amounts of structured, semi-structured, and unstructured data depending on the application needs, using tools like Cassandra and MongoDB.
Machine Learning skills
Machine learning algorithms can categorise incoming data, identify trends, and also transform data into insights.
Integrating machine learning into big data processing can help to speed up the process by revealing trends and patterns.
Understanding machine learning necessitates a solid grounding in mathematics and statistics.
Knowledge of technologies such as SAS, SPSS, and R, listed in your resume can impress hiring managers to find you as a qualifying candidate.
SEE: Cover letter vs resume, Which is more likely to impress recruiters
Cloud computing applications
One of the jobs of big data teams is to set up the cloud to store and assure the high availability of data. As a result, cloud computing skills have become a vital skill to acquire when dealing with big data. Furthermore, companies deploy hybrid, public, or in-house cloud infrastructure depending on their data storage needs. Popular cloud applications are AWS, Azure, GCP, OpenStack and Openshift.
Data mining tools
Data mining is defined as the collection of vital information to detect patterns in an enormous data set. Expert knowledge of data mining tools in your data engineer resume conveys to a hiring manager that you have the set capabilities of a data engineer. Apache Mahout, KNIME, Rapid Miner and Weka are compulsory data mining tools used by data engineers.
Data warehousing and ETL tools
Data warehouses and ETL enable businesses to make effective use of big data. It unifies data from disparate sources.
ETL, or Extract Transform Load, collects data from numerous sources, transforms it for analysis, and puts it into the warehouse. Talend, Informatica PowerCenter, AWS Glue and Stitch are some of the prominent ETL tools.
Data buffering tools
With the rising data quantities, Data buffering has become a critical driver for boosting data processing capability. A data buffer is a region that temporarily holds data while travelling from one location to another.
Kinesis, Redis Cache and GCP Pub/Sub are some of the most utilised tools for data buffering. Having the knowledge of using any of these tools can help you stand a higher chance of getting a job at a hiring company.
Data visualisation tools
Data experts use visualisation tools on a daily basis. Having the knowledge skillset to use tools for data visualisation will also convey to an HR that you have the necessary ability to create insights and learnings in a digestible way for the end-users. Tableau, Qlik, Tibco Spotfire, and Plotly are some of the most used tools for data visualisation.
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