Data Analyst

Key Responsibilities

• Design, plan, and document logical and physical enterprise relational data models.
• Translate logical designs into physical database structures and define end-to-end data flows.
• Implement and maintain physical data models on cloud platforms such as Snowflake.
• Partner with business users to gather data requirements and define KPIs and performance metrics.
• Develop source-to-target mapping documents, including business transformation rules.
• Perform data profiling and data quality analysis to ensure integrity, accuracy, and consistency.
• Support QA and User Acceptance Testing (UAT) and provide ongoing production support for the enterprise data warehouse.
• Identify data issues, perform root-cause analysis, and implement corrective actions.
• Support data governance initiatives through monitoring, validation, and data quality controls.
• Collaborate effectively across development, architecture, data integration, and BI teams.

Required Experience & Qualifications

• Bachelor’s degree in Computer Science, Information Systems, or equivalent professional experience.
• 7–10+ years of overall IT experience in data engineering, data analysis, or software development roles.
• 5–7 years of hands-on data analysis and modeling experience, including complex relational data models.
• 3–5 years of strong Snowflake experience with advanced SQL development.
• Proficiency with SQL and Python for data manipulation and analysis.
• Experience with BI and analytics tools such as Power BI, SAP BO, or Excel.
• Strong understanding of data warehousing concepts, data marts, and analytical database environments.
• Ability to create data flow diagrams and process documentation.
• Excellent analytical, problem-solving, and communication skills.
• Ability to work independently while collaborating across cross-functional teams in a dynamic environment.

Preferred Skills

• Experience in professional services, consulting, or client-facing technology roles.
• Familiarity with BI methodologies, OLAP tools, and enterprise analytics environments.
• Strong business acumen with the ability to communicate data architecture concepts to non-technical stakeholders.
• Comfort working in ambiguous or rapidly changing environments.