Senior Analytics Engineer - Women Applicants Only

About the Area:

An Analytics Engineer is someone who understands business needs while applying best software engineering practices to implement data pipelines and create data abstractions that help the company make better decisions faster.

The workflow of analytics engineering aims to clean, transform, and model raw data into consumable information for strategic decision-making. During the process of obtaining raw data to making clean and transformed data available, the analytics engineer tests the data to ensure quality, documents business rules, and creates models to ensure they are consumed reliably and performantly.

Responsibilities of an Analytics Engineer at QuintoAndar:

• Communicate with different stakeholders to understand their primary needs;
• Create and edit data pipelines, considering business logic that best applies to the area in question, choosing levels of aggregation, grouping and transforming fields, checking data quality, and cleaning the data;
• Investigate inconsistencies and be able to trace the source of differences (data troubleshooting);
• Maintain and promote code and pipeline standards through code review, including testing and documentation;
• Create robust data models that run reliably in a production environment;
• Ensure self-service analytics for business areas

What we are looking for:

• Has experience in building multidimensional data models (Star and/or Snowflake schema);
• Has strong SQL skills and feels comfortable with complex SQL;
• Is familiar with ELT/ETL pipelines;
• Has experience with Python;
• Knows columnar storage solutions (such as Amazon Redshift and Apache Parquet);
• Has 3 or more years of experience building pipelines;
• Is proficient in English and/or Spanish - our code, documentation, tools, and materials are often structured in English. Sometimes, we communicate with colleagues and vendors from other countries;
• Is free of any prejudice

You'll stand out if you:

• Good communication skills and experience working with multiple teams;
• Experience with Airflow, Spark, and Databricks;
• Strong knowledge in optimizing SQL query performance;
• Experience in building semantic layers