Senior Data Engineer
New
B
Blue Orange DigitalData engineering
Source API remote eligibility restrictions: Mexico, PanamaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 7+ years of professional data engineering experience
- Required Skills
- PythonApache AirflowMicrosoft Power BISnowflakeTableaudbt
Requirements
- Have 7+ years of professional data engineering experience.
- Demonstrate ownership of complex projects with minimal direction.
- Have hands-on Snowflake proficiency, including performance tuning and modern Snowflake capabilities.
- Have strong dbt experience building and maintaining production ELT pipelines.
- Have experience with Apache Airflow or a comparable orchestration platform.
- Have strong Python skills.
- Have a solid grounding in dimensional modeling, data warehousing, and analytics engineering best practices.
- Be comfortable with reporting and visualization tools such as Sigma, Tableau, or Power BI.
- Have a track record of designing scalable, maintainable, well-documented pipelines and models.
- Have mentorship experience, including reviewing code and supporting less senior engineers.
- Have a bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- Snowflake Cortex experience or a Snowflake AI certification is a preferred differentiator.
- Sigma experience, Tableau experience with embedded or customer-facing reporting, and experience in a post-acquisition or carve-out data environment are preferred.
Responsibilities
- Own complex technical projects end-to-end, from design through production, with minimal direction.
- Design, build, and maintain scalable ELT pipelines in dbt against a Snowflake warehouse.
- Tune Snowflake queries and warehouse performance.
- Build and maintain orchestration workflows in Apache Airflow.
- Apply dimensional modeling, data warehousing, and analytics engineering practices to support downstream reporting.
- Prepare and model data for modern BI tools, primarily Sigma, and Tableau for embedded customer-facing reporting.
- Identify opportunities for automation, process improvement, and platform modernization.
- Mentor engineers through code review and technical guidance.
- Flag risks and performance issues and help shape future AI/ML data capabilities.
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