Senior Business Analyst

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.