Core Responsibilities: Gather and prioritize requirements from business stakeholders across multiple business segments. Translate business questions into specific, answerable data problems. Perform exploratory data analysis (EDA) to uncover trends, patterns, and anomalies. Apply domain-specific knowledge to identify correlations across datasets. Conduct diagnostic analysis to explain the 'why' behind data trends. Collaborate cross-functionally to validate insights and define data needs. Communicate insights through data storytelling and visualizations. Present multiple scenarios to support decision-making. Worked on large-scale data projects involving direct engagement with business teams and cross-functional teams. Key Skills: Expert-level SQL skills for querying, transforming, and analyzing large datasets efficiently. Strong proficiency in Python, especially with Pandas, NumPy, and other data manipulation libraries. Experience in data analytics, modeling, and exploratory analysis. Familiarity with data governance, catalog tools, and lineage tracking. Ability to translate complex data into actionable business insights. Experience: Led data-driven initiatives that influenced key business decisions and improved operational efficiency. Designed and implemented scalable data solutions in collaboration with engineering and architecture teams. Worked on large-scale data projects involving direct engagement with business teams and cross-functional teams. Defined and communicated data requirements to engineering teams, ensuring alignment with business goals. Acted as a liaison between business teams and data engineering to define data integrity standards and system needs. Developed and maintained dashboards and reporting tools for executive-level visibility. Conducted A/B testing and statistical analysis to evaluate business strategies and product performance. .