Senior Data Analyst

Role Overview: As a dynamic and hands-on Data Analytics & AI professional, you will play a crucial role in our growing team. Your strong foundation in SQL and Python, along with experience in modern data engineering practices, will enable you to lead analytics initiatives and guide junior team members effectively. Your ability to work in a fast-paced environment, handle ambiguity, and collaborate with leadership to drive data-informed decisions will be essential in this role. Key Responsibilities: - Design, build, and maintain data pipelines and analytical workflows following data engineering best practices. - Conduct exploratory data analysis, business intelligence, and AI/ML-oriented problem-solving. - Utilize data visualization tools to create dashboards and reports for decision-making. - Collaborate directly with stakeholders and leadership to understand and translate business requirements into analytics and data solutions. - Mentor and support junior analysts within the team. - Proactively adapt to changing business needs and adjust analytical approaches accordingly. - Be available for PST time zone meetings while working in the IST time zone. Qualification Required: - Strong proficiency in SQL and Python for data manipulation and analysis. - Hands-on experience in designing and building data pipelines, as well as working with large-scale datasets. - Familiarity with data engineering best practices such as modular code, documentation, version control (GIT), and testing. - Experience with at least one data visualization tool (Tableau, Redash, Power BI, etc.). - Excellent communication skills and ability to collaborate with leadership and cross-functional teams. - Ability to thrive in dynamic and fast-changing environments with minimal supervision. - Strong analytical thinking with a bias for action and outcomes. Additional Details: The company prefers candidates with experience in tools like Tableau, Redash, and Databricks. A background in working with e-commerce or customer-focused data products or companies is considered beneficial. Exposure to cloud data platforms (e.g., AWS, GCP, Azure) and experience in working with ambiguous problem statements and hypothesis-driven analysis are also valued. (Note: The company's additional details have been summarized from the provided job description.),