Data Analyst

At DataArt, a Data Analyst designs and maintains Tableau dashboards, oversees key metrics and KPIs, develops data pipelines, and investigates platform issues. They play a crucial role in deriving actionable insights from data to inform business decisions.

To thrive as a Data Analyst at DataArt, you'll ideally have a Bachelor's degree in Statistics, Mathematics, Computer Science, or a related field, along with 2-4 years of experience in data analysis. Proficiency in SQL, Python, or R and experience with data visualization tools like Tableau are key.

DataArt Data Analysts utilize tools such as SQL for data manipulation, Python or R for analysis, and Tableau or Power BI for data visualization. Excel skills, including pivot tables and VBA, are also highly beneficial in this role.

DataArt fosters a people-first culture, promoting autonomy and encouraging professional growth through various learning and development programs. With remote, onsite, and hybrid work options available, you’ll enjoy flexibility in your work environment.

At DataArt, career development for Data Analysts is supported through mentorship programs, unlimited learning and education support, and access to R&D labs, ensuring you have opportunities to enhance your skills and advance your career.

Yes, DataArt provides a comprehensive benefits package including company-sponsored health insurance, unlimited learning support, and generous vacation policies, ensuring a healthy work-life balance.

The Data Analyst at DataArt is primarily responsible for designing dashboards, analyzing data, tracking performance metrics, resolving platform issues, and equipping team members with insights that aid in informed decision-making.

When answering this question, elaborate on specific projects where you used Tableau to create dashboards, emphasizing your ability to translate data into visual formats that help stakeholders understand insights.

Discuss your typical process for data cleansing, including techniques you use to identify and rectify errors, along with tools and methodologies to ensure data quality before analysis.

Provide examples of how you've used SQL to manipulate and query databases, emphasizing specific projects where SQL made a significant impact on data insights or decision-making.

Explain your approach to defining KPIs, how you align them with business objectives, and the metrics you've tracked in the past. Highlight your method for analyzing their impact on performance.

Share a specific instance of a complex data problem, discuss the steps you took to analyze it, the tools involved, and how your resolution impacted the business or project.

Detail the statistical methods you use regularly, such as regression analysis or hypothesis testing, and provide examples of projects where you applied these methods to derive insights.

Describe your communication strategy with stakeholders to gather their requirements, how you maintain stakeholder engagement throughout the analysis process, and how you deliver your findings.

Discuss examples of how you collaborate with other teams like engineering or product management, showcasing your ability to work cross-functionally to enhance data-driven decision-making.

Share your methods for keeping your skills current, such as participating in webinars, online courses, or professional communities, and how implementing new trends has benefited your work.

Provide a concrete example of a time your analysis had a direct impact on a business decision, detailing the context, findings, and ultimate outcome of those findings.