Note: The job is a remote job and is open to candidates in USA. Mammoth Holdings is seeking a Junior Full Stack Data Engineer to join their Data & Analytics team. This role involves designing and maintaining data pipelines, integrating systems, and developing analytics solutions using modern AI tooling to enhance their data platform.
Responsibilities
- Design, build, and maintain ELT pipelines that ingest, clean, and transform data from multiple internal and external source systems into Snowflake
- Develop well-structured, tested, and documented dbt models; write performant SQL for complex transformations across the warehouse
- Own the scheduling, dependency management, and monitoring of engineering pipelines end to end — so jobs run in the right order, failures are caught early, and data lands fresh and on time
- Connect things together — build the integrations that move data between source applications, APIs, the Snowflake warehouse, and downstream consumers across AWS, keeping the whole data flow coherent and reliable
- Go beyond reporting — dig into the data to answer real business questions, validate assumptions, and surface trends and anomalies; bring sound analytical judgment to every dataset you touch
- Build and maintain Power BI dashboards and semantic models that stakeholders rely on daily, with clean data models, solid DAX, and clear visual design
- Use AI/LLM tooling — including MCP servers and AI-assisted development workflows — to automate data tasks, integrate AI capabilities into the platform, and prototype intelligent data services
- Write clean, well-documented, version-controlled code; participate in code reviews; and uphold data quality, testing, and monitoring standards across the stack
- Work closely with senior engineers, analysts, and business partners to scope problems, present findings, and iterate on solutions
Skills
- 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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