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

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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.

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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

Cloud Networking

Commitment:

5 weeks, videos, reading, and practice quizzes.

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: Classification

Commitment:

7 weeks of study, 5-8 hours/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

R Programming

Commitment:

4-weeks, on demand video and reading.

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.

Coursera

Visual Analytics with Tableau

Commitment:

4 weeks of study, 5-7 hours/week.

Industry Advisory Council: Certification Group

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