Junior Full Stack Data Engineer

Role Description

We are seeking a Junior Full Stack Data Engineer to join our Data & Analytics team. This role is for someone who is genuinely strong with databases and great at connecting things together:

• Design and tune the SQL and Snowflake models at the heart of our platform.
• Build and orchestrate the engineering pipelines that move data between systems on AWS.
• Stitch applications, warehouse, and reporting layers into one coherent, reliable flow.
• Bring real analytics under your belt — interrogate data, spot what matters, and turn it into Power BI dashboards and analyses the business trusts.
• Use modern AI tooling, including LLM-based workflows and MCP servers, to make the platform smarter and more automated.
• Partner with senior engineers and business stakeholders to deliver production-grade data products from ingestion through insight.

Qualifications

• 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).

Requirements

• Data Engineering: design, build, and maintain ELT pipelines that ingest, clean, and transform data from multiple internal and external source systems into Snowflake.
• Data Modeling & Transformation: develop well-structured, tested, and documented dbt models; write performant SQL for complex transformations across the warehouse.
• Pipeline Orchestration: 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.
• Systems Integration: 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.
• Analytics: 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.
• Reporting & BI: build and maintain Power BI dashboards and semantic models that stakeholders rely on daily, with clean data models, solid DAX, and clear visual design.
• Applied AI: 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.
• Engineering Practices: write clean, well-documented, version-controlled code; participate in code reviews; and uphold data quality, testing, and monitoring standards across the stack.
• Collaboration: work closely with senior engineers, analysts, and business partners to scope problems, present findings, and iterate on solutions.