Data Analyst

As a Data Analyst at Almedia, you’ll be responsible for designing and building performance-optimized data models using SQL and dbt, automating data validation processes, and taking ownership of data analysis within your domain. You'll work closely with cross-functional teams to meet their data needs while ensuring the accuracy and integrity of our datasets.

To be considered for the Data Analyst role at Almedia, you should have relevant experience as an Analytics Engineer or Data Analyst, along with strong proficiency in data visualization tools like Looker. Additionally, you should be familiar with SQL, Python, and data engineering concepts, and have a solid understanding of data warehousing and modeling.

Almedia is committed to talent development and offers a clear path for growth for Data Analysts. You'll have opportunities to collaborate with experienced professionals, receive meaningful feedback, and take advantage of our equity options. We believe in fostering skills at all levels to cultivate future leaders and specialists.

The work culture at Almedia is collaborative and inclusive, where your contributions are valued. You'll be part of a fast-paced team that prioritizes feedback and innovation. We encourage diversity and support a flexible work environment to help you balance your personal and professional life.

As a Data Analyst at Almedia, you'll work with modern data stacks including dbt, Snowflake, BigQuery, and data visualization tools like Looker. Familiarity with cloud platforms such as AWS or GCP, along with version control systems like Git, will be essential in your day-to-day responsibilities.

Discuss specific projects where you used data visualization tools like Looker to create insightful dashboards. Highlight how your visual representations helped stakeholders make informed decisions and which metrics you focused on.

Emphasize your proactive approach in monitoring data integrity, conducting regular audits, and implementing automated tests. Share any frameworks or tools you use to validate and maintain data quality.

Describe your approach to cross-team collaboration, including how you gather requirements and provide analytical support. Provide examples of successful collaborations that led to positive outcomes.

Share a detailed scenario involving a data inconsistency or performance issue. Explain the steps you took to investigate, the methods you employed to resolve it, and the impact of your solution.

Detail your proficiency in Python for data manipulation and SQL for querying databases. Provide examples of how you used these tools to extract insights from complex datasets.

Discuss the resources you use, such as industry blogs, webinars, or professional courses, to stay informed about developments in data analytics tools and methodologies.

Explain the data warehousing techniques you are familiar with, and provide a specific example of a project where you implemented these concepts to enhance data storage and retrieval efficiency.

Talk about your openness to feedback and how you incorporate stakeholder suggestions into your work. Provide an example of how feedback led to a better outcome in your data analysis.

Reflect on your passion for turning data into actionable insights and improving processes. Share a story that illustrates your enthusiasm for the analytical journey and the impact it can have on a business.

Outline your prioritization strategy, including how you assess urgency and importance for various projects. Illustrate with examples of past experiences where you managed multiple tasks effectively.