
501 - 1000 employees
Mammoth Holdings exists to create a positive, fast, friendly, and clean environment for customers, and to partner with team members in order to help them grow and achieve their personal and professional goals.
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501 - 1000 employees
Mammoth Holdings exists to create a positive, fast, friendly, and clean environment for customers, and to partner with team members in order to help them grow and achieve their personal and professional goals.
• Design and tune SQL and Snowflake models. • Build and orchestrate engineering pipelines that move data between systems on AWS. • Interrogate data and turn it into Power BI dashboards. • Use modern AI tooling to improve the platform.
• Bachelor's degree in Computer Science, Engineering, Information Systems, Mathematics, or a related field — or equivalent practical experience. • Approximately 3 years of professional experience in data engineering, analytics engineering, or a comparable technical role. • Deep database skills: expert SQL — confident writing, optimizing, and debugging complex queries — plus a solid grasp of relational design, indexing/clustering, and query performance. • Hands-on experience with Snowflake (or a comparable cloud data warehouse, with willingness to go deep on Snowflake). • Experience building and maintaining dbt models, including testing and documentation. • Working experience with AWS and its core data services (e.g., S3, Lambda, Glue, IAM), and a track record of connecting systems together — integrating applications, APIs, and data stores into orchestrated, dependable pipelines. • Strong analytics under your belt: proven ability to analyze data rigorously and communicate findings, with hands-on Power BI experience (data models, DAX, well-designed dashboards). • Working knowledge of modern AI tooling — LLM APIs, AI-assisted workflows, and familiarity with MCP (Model Context Protocol) servers or similar integration patterns. • Familiarity with version control (Git) and collaborative development workflows. • Solid problem-solving skills, with the ability to communicate technical results to non-technical audiences. • Experience with orchestration tools such as Airflow, Dagster, or dbt Cloud jobs. • Python for pipeline development, automation, and scripting. • Exposure to containerization (Docker) and infrastructure-as-code (e.g., Terraform). • Experience building or integrating MCP servers, agents, or AI APIs (e.g., Anthropic, OpenAI) into data workflows. • Familiarity with dimensional modeling and warehouse design best practices. • Experience administering or optimizing Snowflake (warehouses, roles, cost management).
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