Key Responsibilities Design and implement data architecture and data models that optimize data processes and improve efficiency. Build and maintain reliable, scalable data pipelines that keep data available and trustworthy in Snowflake or another database or data warehouse. Write and optimize SQL queries to extract, manipulate, and analyze large datasets for business insights. Use Python to integrate, transform, and automate data workflows. Analyze data to find patterns and root causes, and present findings with clear visualizations (Tableau, Sigma, or similar) that support data-driven decisions. Collaborate with cross-functional teams to gather business requirements and translate them into technical specifications. Optimize and streamline existing analytics processes to improve reporting capabilities. Manage relationships with key stakeholders, understand their data needs, and deliver timely, actionable insights. Required Skills Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Information Systems, or a related field. Experience: 4+ years in data analytics, data architecture, and process optimization. Analytical strength: Strong analytical and problem-solving skills, with the ability to break down ambiguous business questions, find patterns and root causes in large datasets, and back conclusions with data and Visualization. SQL: Expert at writing complex queries, tuning query and database performance, and managing large datasets. Architecture and modeling: Designed scalable data models and platforms (dimensional, star/snowflake schemas) that handle high data volumes, with sound pipeline management. Python: Strong working knowledge for data integration, manipulation, and automation. Stakeholder management: Communicates clearly with both technical and non-technical teams. Preferred (Nice to Have) Experience with Snowflake and ELT/orchestration tools (dbt, Airflow), plus Git/CI-CD for analytics code. Data quality, observability, and governance practices, such as validation checks, lineage, and SLAs. Snowflake cost and performance tuning (warehouse sizing, clustering, query profiling). AI-driven analytics: hands-on use of LLMs and AI tools (for example Snowflake Cortex or Claude) for text-to-SQL, automated insights, or anomaly detection.