Analytics Engineer (Remote)

• Perform in-depth data analysis to extract insights and support strategic business decisions.
• Develop, test, and maintain data models using dbt (Data Build Tool) to structure data for analysis.
• Write advanced SQL queries to extract, manipulate, and analyze data, primarily in BigQuery.
• Create and manage visualizations in Looker, working with stakeholders to meet reporting needs.
• Utilize product analytics tools like Heap to analyze user interactions and provide product insights.
• Leverage Python for data manipulation, automation, and advanced analytics tasks.
• Implement version control and collaborative workflows using GitHub for data and code management.
• Support data pipeline optimization and reliability in partnership with other data team members.
• Document processes and data models thoroughly, ensuring knowledge transfer and maintainability.
• Conduct code reviews and assist team members with best practices in data modeling and analysis.
• Translate technical insights into business terms for non-technical stakeholders.

Required Qualifications

• Education: Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field.
• Experience: At least 4 years of relevant work experience in a data analyst or analytics engineer role.
• BI Tools: Experience with data analysis and visualization in BI tools (Looker preferred).
• Data Transformation: Hands-on experience with dbt for data transformation and modeling.
• Advance SQL Proficiency:

• Expertise in advanced window functions (e.g., LAG, LEAD, NTH_VALUE) and complex data transformations.
• Capable of writing dynamic SQL queries and handling nested and repeated fields.
• Skilled in designing and implementing materialized views, partitioning, and clustering strategies to optimize query performance in cloud databases.
• Proficient in understanding and using query execution plans and profiling tools to fine-tune performance.
• Experience with recursive queries, query automation, and integrating SQL with ETL tools (e.g., dbt).
• Strong understanding of data modelling techniques for building scalable and efficient schemas (e.g., Star Schema, Snowflake Schema).