Senior Analytics Engineer
Hace 16 horas
Colombia
MILL5
Trabajo remoto
Jornada completa
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Role Description:
This full-time remote contractor role focuses on analytical data modeling in Snowflake using dbt. The data pipelines are already in place and owned by a separate team. The Senior Analytics Engineer owns the modeling layer: how data is structured, tested, and delivered so that analysts, BI teams, and business stakeholders can use it to make decisions. The team is small. Each member is expected to contribute significantly from the first weeks, propose improvements, and take ownership of problems without waiting for instructions.
Responsibilities:
• Work with business stakeholders to understand analytical needs and translate them into data models. Design dimensional models, data marts, and consumption layers in Snowflake.
• Develop dbt models in clear layers, including incremental models, SCD Type 2 snapshots, tests, and documentation.
• Define aggregation strategies and optimize query performance based on Snowflake architecture: micro-partitions, pruning, clustering, and caching.
• Collaborate with the infrastructure team on data sources. Required
Qualifications:
• 4 or more years of experience in Data Engineering or Analytics Engineering.
• Advanced Snowflake experience in recent production work, including its architecture and a structured approach to query performance.
• dbt experience in recent production work as a standard way of working: layered models, incremental models, snapshots, and tests.
• Both Snowflake and dbt are required.
• Proven experience in analytical data modeling: dimensional models, consumption layers and aggregations. Strong SQL and working knowledge of Python.
• Ability to explain data models and their trade-offs to nontechnical stakeholders.
• English level B2 to C1, validated in a live conversation. Preferred
Qualifications:
• Previous experience with the Analytics Engineer title.
• SnowPro Core certification. It complements production experience but does not replace it.
• Experience with AWS data services. This Role Is Not a Fit For:
• Profiles focused mainly on pipeline development and orchestration.
• Profiles focused mainly on reporting and dashboards.
• Snowflake or dbt experience limited to courses or personal projects. What Success Looks Like: Business teams rely on your models to make decisions, and the models are efficient because their design reflects how Snowflake works.
Responsibilities:
• Work with business stakeholders to understand analytical needs and translate them into data models. Design dimensional models, data marts, and consumption layers in Snowflake.
• Develop dbt models in clear layers, including incremental models, SCD Type 2 snapshots, tests, and documentation.
• Define aggregation strategies and optimize query performance based on Snowflake architecture: micro-partitions, pruning, clustering, and caching.
• Collaborate with the infrastructure team on data sources. Required
Qualifications:
• 4 or more years of experience in Data Engineering or Analytics Engineering.
• Advanced Snowflake experience in recent production work, including its architecture and a structured approach to query performance.
• dbt experience in recent production work as a standard way of working: layered models, incremental models, snapshots, and tests.
• Both Snowflake and dbt are required.
• Proven experience in analytical data modeling: dimensional models, consumption layers and aggregations. Strong SQL and working knowledge of Python.
• Ability to explain data models and their trade-offs to nontechnical stakeholders.
• English level B2 to C1, validated in a live conversation. Preferred
Qualifications:
• Previous experience with the Analytics Engineer title.
• SnowPro Core certification. It complements production experience but does not replace it.
• Experience with AWS data services. This Role Is Not a Fit For:
• Profiles focused mainly on pipeline development and orchestration.
• Profiles focused mainly on reporting and dashboards.
• Snowflake or dbt experience limited to courses or personal projects. What Success Looks Like: Business teams rely on your models to make decisions, and the models are efficient because their design reflects how Snowflake works.