Level III Data Engineer

Blue Margin•Fort Collins, CO

2d•Hybrid

About The Position

At Blue Margin, we’re on a mission to build the go-to data platform for PE-backed mid-market companies. We provide hosted data platforms that help executives and operators turn data into a strategic advantage. We’re passionate about helping clients increase company value through better analysis and decision-making, and we’re looking for a Level III Data Engineer to strengthen our team. As a Level III Data Engineer, you will build, maintain, and improve the data platforms that power analytics for our clients. You will be hands-on with data pipelines, large-scale data processing, and modern cloud data stacks, working within Blue Margin’s architecture and standards. You will also help build and extend the MCP Servers that connect our data platforms to AI and natural-language applications. This role requires solid experience with Python (including PySpark/Apache Spark), experience working with high-volume data, and Delta Lake–based architectures. Exposure to Snowflake or Microsoft Fabric, and tools like Fivetran, Azure Data Factory, Synapse Pipelines, and MCP Servers, is highly valued. On this team, you will help build and maintain our MCP Servers - the servers, their tools, and functionality, along with the performance, reliability, and security behind them. A separate team owns the data modeling and context gathering that power natural-language querying, so your focus stays on building reliable platform capabilities. If you’re motivated by solving complex data challenges, thrive in a collaborative environment, and enjoy applying AI to increase engineering productivity, this role offers the opportunity to grow your craft and make a meaningful technical impact. We are seeking a candidate to work as a full-time employee in one of our local offices in Fort Collins or Denver where we work in a hybrid structure. Please note that we are interested in every qualified candidate who is eligible to work in the United States. However, we cannot sponsor visas.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or related field.
4+ years of professional experience in data engineering, with emphasis on Python & PySpark/Apache Spark.
Good communication skills; ability to explain technical details to both engineers and business stakeholders.
Concrete experience leveraging AI and AI-assisted tools to accelerate engineering workflows.
Experience managing large datasets and optimizing for speed, scalability, and reliability.
Solid SQL skills and understanding of relational and distributed data systems.
Exposure to Azure Data Factory, Synapse Pipelines, Fivetran, Delta Lake, Microsoft Fabric, MCP Servers, or Snowflake.
Familiarity with data modeling, orchestration, and Delta/Parquet file management best practices.
Familiarity with CI/CD, version control, and DevOps practices for data pipelines.
Software engineering fundamentals & familiarity with LLM tool-calling or MCP Server patterns is a strong plus.
Relevant certifications (Azure, Snowflake, or Fabric) are a plus.

Nice To Haves

Software engineering fundamentals & familiarity with LLM tool-calling or MCP Server patterns is a strong plus.
Relevant certifications (Azure, Snowflake, or Fabric) are a plus.

Responsibilities

Build, test, and optimize data pipelines using tools like PySpark, SparkSQL, Delta Lake, and cloud-native tools.
Support incremental/delta data loading, partitioning, and performance tuning.
Implement and support solutions across Azure Synapse, Microsoft Fabric, and/or Snowflake environments.
Collaborate with analysts and senior engineers to turn requirements into working data solutions.
Use AI/automation to improve development speed, testing, and data quality.
Help build and extend custom MCP Servers, including their tools and functionality, that expose our data platforms to AI applications.
Follow software engineering best practices, API and service design, testing, and secure authentication, in platform and MCP Server development.
Follow team coding standards and best practices, and contribute improvements where you see them.
Uphold standards for data quality, security, and governance.
Contribute to solution design for client engagements with the guidance of senior engineers.