As a Senior Data Analyst, you will play a critical role in transforming data into insights that drive strategic decisions. This position involves advanced data handling, business analytics, and stakeholder collaboration. You will be responsible for leading analytical initiatives, ensuring high-quality data practices, and fostering a data-driven culture within the organization.
Overall purpose of job: Integrate and process data from various sources using tools like Databricks, ensuring accuracy, completeness, and consistency. Clean, transform, and preprocess data to address issues such as missing values, outliers, and data quality inconsistencies. Apply descriptive statistics, hypothesis testing, A/B testing, uplift modeling, and basic machine learning models (regression, classification, clustering) to uncover actionable insights. Design and implement Power BI dashboards and visualizations to communicate findings in a clear, engaging, and actionable manner. Collaborate with product managers, engineers, scientists, and business stakeholders to define data requirements and deliver tailored analytical outputs. Support the development and refinement of data models for forecasting, segmentation, and performance tracking. Lead analytical projects, define priorities, and make decisions on the best data strategies. Conduct root-cause analysis and troubleshoot issues related to data quality and performance. Promote a culture of continuous improvement through identification of optimization opportunities in processes, KPIs, and data products. Stay current on emerging trends and technologies in data analytics and contribute to their adoption when relevant.
What you'll need: Bachelor’s degree in Statistics, Mathematics, Computer Science, Economics, or related field. Master’s degree is a plus. 4–5 years of relevant experience in data analysis, preferably in dynamic or digital environments. Advanced skills in SQL, Python and PySpark. Hands‑on experience with Databricks and Power BI (mandatory). Familiarity with data observability concepts and tools. Strong communication skills to present complex findings to both technical and non-technical audiences. Collaborative, detail-oriented, and proactive in identifying opportunities for data-driven improvements. Solid foundation in business analytics and statistical reasoning, including descriptive and inferential statistics, A/B and multivariate testing, uplift modeling, predictive modeling (regression, classification), and clustering techniques.