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: you will 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, and stitch applications, warehouse, and reporting layers into one coherent, reliable flow. Just as important, you bring real analytics under your belt — you can interrogate data, spot what matters, and turn it into Power BI dashboards and analyses the business trusts. You will also use modern AI tooling, including LLM-based workflows and MCP servers, to make the platform smarter and more automated. With roughly three years of professional experience, you will partner with senior engineers and business stakeholders to deliver production-grade data products from ingestion through insight.

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.
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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