33 days paid holiday
40 hours a week (great work-life balance)
Looking for a data analyst for the largest tech company in the world. They are looking to expand their London based team.
What the team does...
BUILD AND DEPLOY NETWORKS AROUND THE WORLD
The team needs someone for...
-Analytics and Measurement
-Investment Hypothesis (should we invest in this project?)
-Really comfortable with Tableau -creating dashboards
-Using data visualisation tools
-Building dashboards- whats an anomaly what’s not?
-Familiar with Product roadmaps
-Q/a validation is a plus
-stong Liaise needed to work cross functionally
-Pull data- make sense of it
-What charts make sense to use?
What risk is the business is taking by choosing this market
If they know networks it’s a (Telco) huge plus)
30-40% actively coding, modeling or building documentation
Interacting with product team, partners, other data teams
20-30%-supporting the team on other adhoc requests
20-30% coordinating cross functionally
The main function of a data analyst is to coordinate changes to computer databases, test, and implement the database applying knowledge of database management systems. A typical database analyst/programmer is responsible for planning, coordinating and implementing security measures to safeguard the computer database.
• Test programs or databases, correct errors and make necessary modifications.
• Modify existing databases and database management systems or direct programmers and analysts to make changes.
• Write and code logical and physical database descriptions and specify identifiers of database to management system or direct others in coding descriptions.
• Leverage tools such as Python or R & SQL to drive efficient analytics.
• Work cross-functionally to define problem statements, collect data, build analytical models and make recommendations.
• Experience working with big data analytics tools, and conducting large scale analysis.
• Experience using analytical tools (Tableau, statistical, visualization, GIS / mapping) and data processing and data analytics techniques (Python, R, SQL).
• Understanding of statistics (e.g., hypothesis testing, statistical inference, regressions, ML systems).
• Verbal and written communication skills, problem solving skills, customer service and interpersonal skills.
• Basic ability to work independently and manage one’s time.