Data Applications Engineer
Applicants must have a minimum of two years experience in analytics or a related area in addition to the following criteria:
Skills
Knowledge
Applicants must have confidence in at least one skillset under each heading:
Analytics
Alteryx
Languages
SQL
T-SQUL (Stored Procedures and Functions)
PL/SQL
phPL/Sql
Pig
Hive
Impala
Shell Scripting
DOS Batch
Power Shell
BASH (UNIX/LINUX utilities)
Techniques & Methods
Big Data Concepts
Data Warehousing
Analytics
Alteryx
Languages
SQL
T-SQUL (Stored Procedures and Functions)
PL/SQL
phPL/Sql
Pig
Hive
Impala
Shell Scripting
DOS Batch
Power Shell
BASH (UNIX/LINUX utilities)
Techniques & Methods
Big Data Concepts
Data Warehousing
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Applicants must be able to demonstrate some capability in the following areas:
Develop and apply computational and data driven solutions to domain related problems using wide range of data analytics platforms, including Big Data technologies for large datasets and cloud based data analytics platforms.
Develop, deploy and operate large scale data storage and processing solutions using different distributed and cloud based platforms for storing dataConsistently apply data security mechanisms and controls at each stage of the data processing, including data anonymisation, privacy and IPR protection.
Design, build, operate relational and nonrelational databases (SQL and NoSQL), integrate them with the modern Data Solutions, ensure effective ETL (Extract, Transform, Load), OLTP, OLAP processes as appropriate to the Data Application being engineered.
Use engineering principles and modern computer technologies to research, design, implement new data analytics applications; develop experiments, processes, instruments, systems, infrastructures to support data handling during the whole data lifecycle.Use engineering principles (general and software) to research, design, develop and implement new instruments and applications for data collection, storage, analysis and visualisation.
Develop and prototype specialised data analysis applicaions, tools and supporting infrastructures for data driven scientific, business or organisational workflow; use distributed, parallel, batch and streaming processing platforms, including online and cloud based solutions for on-demand provisioned and scalable services.
Visualise results of data analysis, design dashboard and use storytelling methods
Develop and apply computational and data driven solutions to domain related problems using wide range of data analytics platforms, including Big Data technologies for large datasets and cloud based data analytics platforms.
Develop, deploy and operate large scale data storage and processing solutions using different distributed and cloud based platforms for storing dataConsistently apply data security mechanisms and controls at each stage of the data processing, including data anonymisation, privacy and IPR protection.
Design, build, operate relational and nonrelational databases (SQL and NoSQL), integrate them with the modern Data Solutions, ensure effective ETL (Extract, Transform, Load), OLTP, OLAP processes as appropriate to the Data Application being engineered.
Use engineering principles and modern computer technologies to research, design, implement new data analytics applications; develop experiments, processes, instruments, systems, infrastructures to support data handling during the whole data lifecycle.Use engineering principles (general and software) to research, design, develop and implement new instruments and applications for data collection, storage, analysis and visualisation.
Develop and prototype specialised data analysis applicaions, tools and supporting infrastructures for data driven scientific, business or organisational workflow; use distributed, parallel, batch and streaming processing platforms, including online and cloud based solutions for on-demand provisioned and scalable services.
Visualise results of data analysis, design dashboard and use storytelling methods
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Apply for Certification
If your skills, knowledge and experience meet the minimum requirements as set out in the framework, you can apply today to be assessed for certification.
Our certification committee meets to assess applicants on a regular basis. You should expect to hear from us within six weeks of applying.
Click Register Now and complete an online application form, uploading the required evidence:
- CV.
- LinkedIn Profile.
- Certificates of Education (Degrees/Diploma).
- Any Project Work details you wish to include to support your application.
Not yet ready for Certification?
If you’ve decided you’re not ready for certification, we’ve outlined a programme of online learning with Coursera© to get you there. Mapped to our framework, these courses will give you the necessary skills to complete certification in your chosen area of expertise. Coursera© courses can be completed in your own time and typically cost €49.
Simply choose the courses that fill the gaps in your knowledge. Once completed, you can return and register for certification, uploading your Coursera© certificates to support your application.
European Data Science Framework
Our certification is mapped to the Edison Framework (EDSF).
A Europe-wide project to establish the necessary skills and define Data Science as a professional practice across the continent.
Not yet ready for Certification?
If you’ve decided you’re not ready for certification, we’ve outlined a programme of online learning with Coursera© to get you there. Mapped to our framework, these courses will give you the necessary skills to complete certification in your chosen area of expertise. Coursera© courses can be completed in your own time and typically cost €49.
Simply choose the courses that fill the gaps in your knowledge. Once completed, you can return and register for certification, uploading your Coursera© certificates to support your application.
Coursera
Cloud Computing Applications, Part 1
Commitment:
5 weeks of study, 5 - 10 hours/week.
Coursera
Cloud Computing Applications, Part 2
Commitment:
5 weeks of study, 5 - 10 hours/week.
Coursera
Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions
Commitment:
4 weeks of study, 1-3 hours/week.
Coursera
Introduction to Recommender Systems: Non-Personalized and Content-Based
Commitment:
4 weeks; an average of 3-7 hours per week.
Coursera
Machine Learning: Clustering & Retrieval
Commitment:
6 weeks of study, 5-8 hours/week.
Coursera
Hadoop Platform and Application Framework
Commitment:
5 weeks of study, 1-2 hours/week.
Coursera
An Introduction to Interactive Programming in Python (Part 1)
Commitment:
5 weeks of study, 7-10 hours/week.
Coursera
An Introduction to Interactive Programming in Python (Part 2)
Commitment:
4 weeks of study, 7-10 hours/week.
Coursera
Data Manipulation at Scale: Systems and Algorithms
Commitment:
4 weeks of study, 6-8 hours/week.
Industry Advisory Council: Certification Group
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Managing Director: Lorcan Malone
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