Data Analyst

Responsible for the end-to-end management and manipulation of large datasets, transforming raw data into actionable insights. The role requires a combination of technical data engineering skills and analytical problem-solving abilities, using Python and complex regex patterns to clean, standardize, and transform the data.

Core Responsibilities: Ingest, process, and manipulate large, often unstructured, datasets (100,000+ rows). Clean, standardise, and transform data using advanced scripting and regular expressions. Design and implement intelligent mapping logic to match and reconcile records across disparate datasets. Contribute to data transformation, workflow automation, and process optimisation initiatives. Translate complex data processes and findings into clear, actionable insights and summaries for various stakeholders.

Data Analysis & Engineering: Advanced Google Sheets Expertise: Proficiency in Pivot Tables. Extensive experience with Regular Expressions (REGEX functions). Mastery of ARRAYFORMULA and other advanced Google Sheets formulas. Intermediate Python Knowledge: Hands-on experience with Pandas for data manipulation. Experience with PySpark for large-scale data processing. Intermediate SQL Proficiency: Strong understanding and application of SQL Joins. Expertise in string manipulation and null handling functions (e.g., CONCAT, COALESCE). Regular Expressions (REGEX): Proven ability to use complex regex patterns for data cleaning and transformation. Automation & Scripting: Understanding of Google Apps Script. Ability to leverage AI tools to develop simple automation solutions (e.g., bulk extraction from Google Drive). Data Handling: Experience working with large, unstructured datasets.

Business Analysis & Problem Solving: Analytical Acumen: Strong analytical and problem-solving skills, with the ability to approach complex challenges creatively.

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