Senior Analytics Engineer
New
N
NicheEducation technology
We are currently recruiting for this role in Argentina and Brazil only. We will not be considering US based candidates at this time.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 6+ years of experience in analytics engineering, data engineering, or enterprise data modeling
- Required Skills
- PythonSQLGitSnowflakeTableaudbtLooker
Requirements
- Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field, or equivalent practical experience.
- 6+ years of experience in analytics engineering, data engineering, or enterprise data modeling.
- Experience architecting, deploying, and maintaining production-grade data transformation workflows and enterprise semantic models.
- Expertise in dimensional data modeling, including Star Schema and Kimball methodology.
- Experience with dbt and semantic-layer tools to standardize complex metric logic across organizations.
- Experience modeling complex business domains, product usage metrics, and unstructured or semi-structured JSON data.
- Strong software engineering background applied to data, including advanced Git workflows, CI/CD, modular architecture, and code documentation.
- Advanced proficiency in SQL, dbt macros and packages, dbt performance tuning, Snowflake, Python, and Git.
- Experience with modern BI platforms such as Looker or Tableau.
- Expertise with Google Analytics, marketing/ad/social media platforms, and CRM/Salesforce systems.
- Understanding of modern data stack architecture, transformation strategies, semantic-layer implementation, automated testing, governance, and BI integrations.
Responsibilities
- Partner with executive stakeholders to translate business strategy into analytics architecture, governance, and self-service capabilities.
- Architect, document, and maintain Kimball dimensional models, standardized metric definitions, warehouse transformation logic, and BI datasets.
- Design, build, and govern data models that power the enterprise semantic layer and support AI/ML integrations and self-service analytics.
- Establish data quality, automated testing, lineage tracking, cost optimization, and alerting frameworks across dbt pipelines.
- Evaluate and refactor legacy business logic and fragmented query architectures into modular, scalable semantic pipelines.
- Drive data governance, metric standardization, and secure access controls across data domains.
- Lead analytics platform and semantic-layer initiatives and own key domain models.
- Mentor junior and mid-level engineers and establish technical best practices.
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