- 6-8 years of experience in a data-related role (e.g., Data Analyst, Business Intelligence Analyst, or Data Engineer).
- Advanced SQL skills: Capable of writing complex queries, optimizing query performance, and working with large datasets from relational databases (e.g., MySQL, PostgreSQL, SQL Server).
- Beginner-level Python proficiency: Comfortable using Python for basic data wrangling, such as cleaning, transforming, and analysing datasets.
- Experience working with data visualization tools (e.g., Power BI, Tableau) or the ability to generate reports directly from SQL.
- Proven experience working with business stakeholders to gather requirements and translate them into technical solutions.
- Strong analytical and problem-solving skills, with a keen eye for detail and data accuracy.
- Excellent communication skills, both verbal and written, with the ability to present data-driven insights clearly to non-technical audiences.
- Ability to work independently, prioritize tasks, and manage multiple projects in a fast-paced environment.
Data Analysis & Reporting:
- Write and optimize complex SQL queries to extract, transform, and analyse large datasets from multiple sources.
- Use Python to perform basic data wrangling, preprocessing, and ad-hoc analysis.
- Generate business insights and actionable reports using data visualization tools or SQL-based reports.
- Build and maintain dashboards and ad-hoc reports to meet the evolving needs of the business.
Data Wrangling & Processing:
- Use Python scripts for automating repetitive tasks, handling datasets, and performing basic data analysis.
- Clean, merge, and manipulate datasets to prepare them for further analysis or reporting.
Stakeholder Collaboration
- Act as a liaison between business and technical teams, gathering requirements from non-technical stakeholders and translating them into technical specifications.
- Work with cross-functional teams (e.g., product managers, marketing, finance) to understand business problems, offer insights, and suggest data-driven solutions.
- Present complex data findings in a simple, understandable manner to business users and management.
Technical Documentation:
- Create and maintain clear documentation of data structures, processes, and reports to ensure that stakeholders can understand and use data effectively.
- Ensure consistent, accurate, and up-to-date documentation of all reporting processes.
Data Integrity & Quality:
- Maintain a high level of data integrity and data quality, ensuring all analyses and reports are reliable and accurate.
- Identify and resolve data quality issues by working with other teams and improving data governance practices.