The Data Analyst role is embedded within a business domain and reports to a domain manager. Areas of work include, but are not limited to, commerce, finance, compliance, marketing, sales, customer service, human capital, and product.
In this role, the Data Analyst will collaborate cross-functionally to understand business problems and then create data-driven solutions to solve them, having the ability to work on a full spectrum of descriptive, diagnostic, predictive and prescriptive analytics. . Data analysts work closely with the Business Intelligence team, benefiting from their data architecture, tools and technical expertise.
Duties and Responsibilities (includes but not limited to):
- Work closely with stakeholders at all levels and across business areas to determine data and analytics requirements
- Translate requirements into reporting or data analysis specifications using critical thinking to ensure alignment with real business needs.
- Be the translator between the Business, Product and Data teams on data needs
- Interview subject matter experts and ghost workflows to better understand our data collection and logging processes. Eventually, become an expert on our data.
- Specify, gather, cleanse, combine and aggregate data from disparate sources for data analysis and reporting needs
- Profile and validate all data intended for use in reports or analyzes to ensure high data quality (accuracy, completeness, correction of missing/invalid data, timeliness)
- Perform data analysis for (non-exhaustive list):
- Extract key insights and insights to influence/support business decisions and actions
- Identify the main drivers/levers and segments that influence the movement of key indicators/KPIs
- Identify gaps in existing business processes and product offerings and make recommendations to improve them
- Measure the effectiveness of product launches and new business initiatives
- Design, create and maintain reports to track and communicate KPIs and results of data analysis activities
- Create data visualizations to improve access and visibility of the content of all key reports and our data in general
- Share insights and analysis results in written form and in business presentations
- Train and support the user community in the use of reports, BI tools and the interpretation of analysis results
- Collect and apply feedback from sales presentations, user training and peer review to continuously improve reporting and analytics solutions
- Proactively communicate the status (including blockers, risks, issues) and roadmap of the work assigned to you, with all relevant stakeholders
- Cultivate relationships and collaborate cross-functionally to shape, support and execute business and product goals
- Help define and improve our analytical standards through participation in the community of practice, conducting peer reviews and contributing to internal documentation
- Identify data quality issues, their corresponding root causes, and collaborate with business, product, and data teams to improve data hygiene
- Continuously improve your skills and stay abreast of new developments in data analysis and BI tools, methodologies and applications
- Grade 12 or equivalent (Essential)
- Advanced degree in a quantitative field (includes but not limited to math, statistics, engineering, computer science, actuarial science, economics, finance, business analysis)
- 4+ years of work experience in data analytics or business intelligence (Essential)
- Strong verbal and written communication skills in English and understand how to share ideas and analytical results with technical and non-technical audiences.
- Knows how to work with stakeholders to gather data and analysis requirements and communicate results
- Strong report/dashboard development and data visualization skills with modern BI tools (e.g. Power BI, Tableau, Qlik)
- Advanced SQL skills
- Advanced skills in Excel
- R or Python programming for data analysis (desirable)
- Experience cleaning, ingesting, manipulating and aggregating large datasets using SQL, R or Python or similar tools
- Knowledge of data analysis techniques, such as time series analysis, scenario analysis, clustering and segmentation, regression, decision trees, forecasting, interpretation, and creating power distributions probability (mastering a few of these will suffice)
- Analytical and problem solving skills
- Execution oriented and able to complete tasks independently
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