Data engineers design, build, program and manage the big data information systems that transforms raw data into useful information that can be mined and applied by businesses.
As software engineers, they develop algorythms and other machine learning mechanisms that power data analytics and oversee the efficient storage, access, distribution and validity of information across an organization.
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- Sub-sector Exploration and production, Oil sands, Oil and gas services, Pipelines
- Environment Primarily indoor/office work
- Education Post-secondary degree
- Career Demand Stable
In this occupation activities may include:
- Conducting initial exploration of data and cleaning the data (removing data that may have errors).
- Developing custom data models and algorithms to apply to data sets.
- Providing insight to leading analytic practices, designing and overseeing interactive learning and development cycles.
- Using Machine Learning and Artificial Intelligence to improve and optimize operations, revenue generation and other business outcomes.
- Interpreting, translating and communicating analytical findings to business stakeholders.
- A bachelor’s degree, usually in computer science, computer systems engineering, software engineering or mathematics or completion of a college program in computer science is usually required.
- A master’s or doctoral degree in a related discipline may be required.
- Licensing by a provincial or territorial association of professional engineers is required to approve engineering drawings and reports and to practise as a Professional Engineer (P.Eng.).
- Engineers are eligible for registration following graduation from an accredited educational program, three or four years of supervised work experience in engineering, and passing a professional practice examination.
- As an emerging occupation, requirements are evolving. These may include:
- Proficiency with analytics scripting languages such as Python, R, SQL and statistical analysis environments such as MATLAB, SPSS or SAS
- Experience with lamda architectures and batch and real-time data streams
- Experience in industry data science (e.g., machine learning, predictive maintenance)
- Experience with agile or other rapid development methods
- Experience with relational databases such as MS Sql Server, PostgreSQL, Oracle
- Experience with data architectures such as Operational Data Store, Kimball and data virtualization
- Experienced in object oriented design, coding and testing patterns as well as experience in engineering software platforms and large-scale data
- Specific health and safety certifications may be required, determined by location of work and company requirements
- Minimal or no travel
- Primarily indoor/office work
- Work not physically demanding
You determine how a system should work and how changes in conditions, operations and the environment will affect outcomes, and you identify actions to improve or correct performance.
- Computers and Electronics
- Customer and Personal Service
- Design Creation
- Application of Engineering Design and Technology
- Information and Document Management
- Cyber/Data Security
- Data Entry
- Equipment Selection
- Operation and Control
- Operations Design Analysis
- Systems Evaluation and Analysis
- Technology Design
- Complex Problem Solving
- Professional Judgment and Decision Making
- Planning and Organizing