Data Analytics

Core Responsibilities

• Data Extraction & Ingestion: Write complex SQL queries to pull primary data from internal data lakes, warehouses, and secondary third-party APIs.
• Data Cleansing & Validation: Audit raw data pipelines to eliminate duplicates, resolve missing entries, and fix broken formatting before processing.
• Dashboard Design & Reporting: Develop, refresh, and maintain automated, self-serve visualization dashboards for company stakeholders.
• Trend & Statistical Analysis: Run diagnostic analysis using Python or R to identify historical trends, interpret KPIs, and evaluate business performance variances.

Required Technical Skills & Toolsets

An ideal candidate must be highly proficient across several distinct layers of the modern data stack:

• Business Intelligence (BI): Mastery of interactive dashboard creation using Tableau, Power BI, or Looker.
• Core Analytics: Strong foundational knowledge of Advanced Excel (Index/Match, Power Query, pivot tables).
• Database Management: Deep proficiency in SQL (PostgreSQL, MySQL, BigQuery, or Snowflake) for complex data retrieval.
• Programming Languages: Hands-on experience executing data manipulation workflows via Python (Pandas, NumPy) or R.

Soft Skills and Traits

• Critical Thinking: Asking the right questions of data rather than simply running queries.
• Problem-Solving: The capacity to look at messy data systems, track errors, and restructure pipelines logically.

Communication: Explaining why a metric dropped or spiked without overloading stakeholders with unnecessary technical jargon