Data Analyst

Job Description :

Responsibilities :

- Ability to identify patterns, trends, and anomalies in log data to generate actionable insights for product enhancements and feature optimization.

- Collaborate with cross-functional teams to gather business requirements and translate them into functional and technical specifications.

- Manage and organize large volumes of application log data using Google BigQuery.

- Design and develop interactive dashboards to visualize key metrics and insights using any of the tools like Tableau, Power BI, or ThoughtSpot AI.

- Create intuitive, impactful visualizations to communicate findings to teams, including customer success and leadership.

- Ensure data integrity, consistency, and accessibility for analytical purposes.

- Analyse application logs to extract metrics and statistics related to product performance, customer behaviour, and user sentiment.

- Work closely with product teams to understand log data generated by Python-based applications.

- Collaborate with stakeholders to define key performance indicators (KPIs) and success metrics.

- Can optimize data pipelines and storage in BigQuery.

- Participate in the development of proof-of-concepts (POCs) and pilot projects.

- Ability to articulate ideas and points of view clearly to the team.

- Take ownership of data analytics and data engineering solutions.

Requirements :

- 5+ years of experience in dashboard story development, dashboard creation, and data engineering pipelines.

- Hands-on experience with log analytics, user engagement metrics, and product performance metrics.

- Strong communication and teamwork skills.

- Ability to learn quickly and adapt to new technologies.

- Excellent problem-solving skills.

Nice-to-Haves :

- Knowledge of Generative AI (GenAI) and LLM-based solutions.

- Experience in designing and developing dashboards using ThoughtSpot AI.

- Good exposure to Google Cloud Platform (GCP).

- Data engineering experience with modern data warehouse architectures.

- Experience working with large datasets and distributed data processing tools such as Apache Spark or Hadoop.

- Familiarity with Agile development methodologies and version control systems like Git.

- Familiarity with ETL tools such as Informatica or Azure Data Factory.