Analytics Engineer – Data & Analytics
New
R
Rankings.ioMarketing analytics
Colombia, Availability for scheduled collaboration during US Central or Eastern Time hours.ContractMiddle
Salary3,300 - 4,100 USD per month
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Job Details
- Languages
- Clear written English
- Experience
- 3+ years of hands-on experience in analytics engineering, data engineering or BI development.
- Required Skills
- PythonSQLGitPandasCI/CDTerraformBigQuerydbt
Requirements
- Have 3+ years of hands-on experience in analytics engineering, data engineering or BI development.
- Demonstrate advanced SQL skills, including CTEs, window functions, complex joins and query optimization.
- Have experience structuring modular data layers from staging through marts, and writing schema and data tests.
- Have experience documenting models with dbt or a similar framework.
- Use Python for data manipulation with pandas, API integrations and automation.
- Have hands-on experience with a cloud data warehouse; BigQuery is preferred, with Snowflake, Databricks or Amazon Redshift also welcome.
- Have experience building clean, performant reports in Looker Studio, Power BI or Tableau.
- Be proficient with Git, standard CI/CD workflows and code review practices.
- Write clear English documentation and pull requests and support asynchronous work with a U.S.-based team.
- Be available for scheduled collaboration during US Central or Eastern Time hours.
- Maintain Huntress endpoint security software for the duration of the engagement while accessing company systems and client data.
Responsibilities
- Build and maintain dbt models from staging through marts and reporting layers in BigQuery.
- Turn newly onboarded data sources into tested, documented marts.
- Write schema and data tests and monitoring to protect data accuracy and lineage.
- Connect accounting, CRM and marketing datasets into unified models for cross-system reporting.
- Build performant Looker Studio dashboards and reports for client teams and internal stakeholders, including Finance.
- Troubleshoot and optimize SQL and model runtimes using partitioning, clustering and incremental models.
- Maintain documented, reliable models for AI tools querying the warehouse, and use AI assistants for SQL, testing and documentation work.
- Translate business requirements into data solutions with stakeholders and the Data & Analytics team, using Git pull requests and code reviews.
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