LaunchGood · 4 hours ago
Analytics Engineer
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Responsibilities
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.
Qualification
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Required
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).
Version Control: Familiarity with GitHub for version control and collaboration.
Product Analytics: Knowledge of tools like Heap for user behavior analysis.
Programming: Experience with Python for data manipulation and automation.
Skills: Strong analytical, problem-solving, and data visualization skills.
Communication: Excellent communication skills with the ability to translate technical findings into business insights for non-technical audiences.
Teamwork: Ability to work both independently and collaboratively within a team environment.
Detail-Oriented: High attention to detail and commitment to producing reliable, high-quality work.