Core Job Responsibilities

• Assists in the deployment and monitoring of data pipelines and workflows under guidance from senior engineers.

• Supports basic data quality initiatives & integration through testing and validation processes.

• Builds basic analytic data sets for exploration and modeling.

• Assists in the implementation of data security measures, compliance, standards, and policies.

• Documents data processes, procedures, and workflows for knowledge sharing.

• Assist with data pipeline development, data cleaning and preprocessing.

• Works with Sr Engineers to understands how business needs are converted into analytical/technical requirements; provides input to data gathering.

• Assists in the scoping, planning and delivery of projects.

• Assists in data collection and cleansing efforts; focused on building and maintaining the infrastructure processes that automate data collection and cleansing.

• Understands the fundamentals of big data storage and distribution systems and relevant software tools, their constraints, advantages and disadvantages.

• Maintains awareness of trends and best practice approaches

Supervisory / Management Responsibility

• Receives specific direction and guidance from more experienced team members. Adapts to changing priorities as they arise.

• Completes own work details and assignments of low complexity and guidance from senior level engineers.

• Collaborates for results and participates in idea-sharing and solution development.

• Escalates issues to more senior colleagues as required.

• Works well with others in a collaborative, goal-driven environment.

Position Accountability/ Scope

• Contributes to multiple projects to support analytical tasks, with a moderate to high level of support provided by colleagues.

• Relevant skills include data mining, informatics, programming, information retrieval, databases and data structures, software engineering and systems design analysis.

• Familiar with a range of mainstream commercial and open-source data tools, their constraints, advantages, disadvantages and areas of application; Has intermediate skill in using at least one such tool.

• Familiar with data and statistical programming languages (such as SQL, SAS, R and Python). Basic programming skills. Ability to interpret an existing script of moderate complexity

• Conducts data acquisition from relational databases and flat files.

• Wrangles low-complexity data, selecting appropriate techniques, such as parsing, or an algorithm, to create a data structure relevant to the problem.

• Seeks mentorship opportunities in the organization to gain a diverse perspective.