Data Scientist, Cape

• 4+ years of experience in Analytics Engineering, Data Engineering, or Business Intelligence, with ownership of production analytics systems
• 2+ years of hands-on experience modeling analytics-ready data using dbt with SQL and/or Python
• Expert-level SQL, including writing, optimizing, and debugging complex analytical queries
• Proven ability to translate complex data into trusted models, metrics, and visualizations used by senior stakeholders
• Deep experience with tools across the modern data stack
• Querying MPP analytical databases (Snowflake, Databricks, Redshift, BigQuery, etc)
• Building out BI (e.g., Omni, Tableau, Looker, Power BI, Sigma, Mode, Hex)
• Orchestrating data pipelines (e.g. Airflow, Dagster, Fivetran)
• Building transformations and semantic layers (e.g. dbt)
• Working proficiency in Python

Desirable

• We are relentless builders, constantly innovating and pushing the boundaries of what a telco can do. At Cape we trust our teammates to be great, and expect them to make profound impact
• As a member of our team, you will collaborate with some of the smartest engineers, architects, and builders anywhere, working across organizational lines to deliver for our customers, every time
• We are looking to add our first data focused hire to build our data infrastructure from the ground up
• You will be the trusted data partner across the company to (i) collaborate with engineering to capture our data needs while adhering to our privacy principles, (ii) build our data stack and implement high quality data pipelines, and (iii) create dashboards to track KPIs
• The ideal candidate is a generalist who wants to split their time between building technical foundations and making sure teams can quickly run their most critical analyses
• This role reports to our Head of Finance
• Own data pipelines: Design, build, and maintain reliable ETL/ELT pipelines, ensuring timely and accurate delivery of high-quality data across the organization
• Create dashboards: Design and implement dashboards, while automating regular reporting workflows to reduce manual effort and increase data consistency
• Collaborate cross-functionally: Work closely with engineering and business teams to understand use cases and proactively develop data assets that support their goals effectively and scalably
• Improve data quality, discoverability, and metric clarity: Design and implement robust systems for schema design, data validation, documentation, and governance, while defining and standardizing core business metrics and semantic definitions to ensure consistency across teams and tools
• Support self-serve insights: Enable teams with intuitive, trustworthy data products and tooling that allow less-technical users to explore data and develop solutions independently
• Priority projects:
• Implement foundational tooling for our data stack (data warehouse, orchestration and transformation tools, dashboards, etc)
• Operationalize schemas needed to run high quality analytics and prepare for usage based billing models
• Partner with leaders to define and monitor key metrics across the business
• 4+ years of experience in Analytics Engineering, Data Engineering, or Business Intelligence, with ownership of production analytics systems
• 2+ years of hands-on experience modeling analytics-ready data using dbt with SQL and/or Python
• Expert-level SQL, including writing, optimizing, and debugging complex analytical queries
• Proven ability to translate complex data into trusted models, metrics, and visualizations used by senior stakeholders
• Deep experience with tools across the modern data stack
• Querying MPP analytical databases (Snowflake, Databricks, Redshift, BigQuery, etc)
• Building out BI (e.g., Omni, Tableau, Looker, Power BI, Sigma, Mode, Hex)
• Orchestrating data pipelines (e.g. Airflow, Dagster, Fivetran)
• Building transformations and semantic layers (e.g. dbt)
• Working proficiency in Python
• Preferred
• Experience building and operating production data pipelines or data platforms, with strong software engineering practices (testing, CI/CD, code review)
• Experience with APIs and other integrations
• Familiarity with AWS and self-hosted installs
• Experience with analytics in privacy-preserving environments