1. Querying Databases: Utilize expertise in SQL and other query languages to extract,

transform, and analyze data from diverse databases, ensuring accuracy, efficiency, and

security.

2. Visual Insight Generation: Analyze large datasets to identify patterns, trends, and

relationships, and transform complex data into actionable visual insights that support

decision-making processes.

3. Dashboard and Report Creation: Develop interactive dashboards and reports using data visualization tools such as JQuery, Tableau, Power BI, or similar tools, ensuring

user-friendly and visually appealing representations of data.

4. Data Analysis: Conduct in-depth analysis of sales data, production metrics, and other

relevant data sets to identify opportunities for process optimization, cost reduction, and

revenue enhancement.

5. Collaborative Partnerships: Work closely with cross-functional teams, including Sales,

Production, and the Executive teams, to understand their data and analysis needs and

provide timely and accurate reports, dashboards, and ad hoc analyses.

6. Data Integrity and Quality Assurance: Implement data governance practices and ensure data accuracy, consistency, and reliability throughout the data analysis process. Conduct data quality assessments and troubleshoot data-related issues.

7. Continuous Improvement: Stay updated on emerging trends, techniques, and best

practices in data analysis and visualization. Proactively identify opportunities to enhance

data analytics processes and provide recommendations for improvement.

8. Collaboration: Work closely with the Sales, Production, and Executive teams to

understand and maintain focus on their analytics needs, including critical metrics and

KPIs, and deliver actionable insights to relevant decision-makers.

9. Professional Analysis: Proactively analyze data to answer key questions for stakeholders before they ask, with an eye on what drives business performance, and investigate and communicate which areas need improvement in efficiency and productivity.

10. Connect Databases: Define and implement data acquisition and integration logic,

collaborating with Developer resources to develop an appropriate combination of

methods and tools within the defined technology stack to ensure data integrity and

performance of the solution.

11. Aggregate real-time data: Develop and maintain databases by acquiring data from

primary and secondary sources, and build scripts that will automate our data evaluation

process more flexible or scalable across datasets.