Data Analyst

As a Data Analyst at Financial Automation in Limassol, your primary responsibility will be to identify and fulfill the reporting needs of Finance stakeholders. This involves implementing changes to current reporting processes, developing new reporting solutions, and enhancing efficiency through automation while ensuring high data quality.

To apply for the Data Analyst position at Financial Automation, you should have at least 2 years of experience in data analytics or data engineering, with strong problem-solving skills. Proficiency in SQL, especially PostgreSQL, is essential, along with the ability to thrive in a fast-paced environment.

While not mandatory, experience in a regulated environment is beneficial for the Data Analyst role at Financial Automation. It helps in understanding the nuances of regulatory reporting and compliance, which is a core part of the job.

As a Data Analyst at Financial Automation in Cyprus, you can expect numerous benefits including medical insurance coverage, participation in a provident fund, flexible working hours, and the chance to work in a hybrid model. Additionally, our workation policy fosters a great work-life balance.

Yes, a Data Analyst at Financial Automation has the option to work in a hybrid model, which allows for a blend of remote work and in-office collaboration. This flexibility is aimed at promoting work-life balance while maintaining productivity.

A Data Analyst at Financial Automation should be familiar with SQL, particularly PostgreSQL, and have experience with BI tools like Tableau. Additionally, knowledge of JIRA, GIT for version control, Airflow for workflow management, and Python for data automation will be advantageous.

As a Data Analyst at Financial Automation, effective communication with internal stakeholders is crucial. You will need to liaise about regulatory reporting processes and relay technical information clearly, helping bridge the gap between engineering tasks and stakeholder needs.

In interviews, highlight your SQL experience by discussing specific projects where you used SQL to manipulate data, derive insights, or automate reporting. Be prepared to showcase your proficiency, particularly with PostgreSQL if applicable.

When discussing problem-solving, outline a systematic approach you take to identify root causes, gather input from stakeholders, and implement effective solutions. Examples showcasing your logical thinking and creativity will strengthen your response.

Share a specific instance where you automated a reporting process, detailing the steps you took, the tools you used, and the impact it had on efficiency or data accuracy. Quantifying the results can make your answer more compelling.

Explain your strategies for effective communication and active listening when gathering requirements. Mention your experience with stakeholder interviews or workshops to clarify and prioritize their needs effectively.

Explain your time management techniques, such as prioritizing tasks based on urgency and complexity. Discuss tools you use to track progress and maintain productivity under pressure, providing examples when possible.

Discuss your experience with BI tools like Tableau, emphasizing how you utilized them to visualize data and derive insights. Mention specific projects where these tools aided in making informed decisions or improving reporting accuracy.

Share your experience using Python for data analysis tasks, such as data cleaning, manipulation, or automation of reports. Highlight specific libraries like Pandas or NumPy that you've used to enhance your analytical capabilities.

Discuss your understanding of regulatory frameworks and your research process to stay updated. Explain how you integrate compliance checks into your analysis workflows to ensure data integrity and adherence to regulations.

Articulate your understanding of potential challenges such as data accuracy or compliance issues. Discuss how you would collaborate with finance teams to address these challenges while ensuring smooth reporting processes.

Highlight the importance of teamwork in your role as a Data Analyst. Discuss how collaboration with teams across departments contributes to comprehensive analysis and improves decision-making, fostering a shared understanding of data insights.