Sr. Data Engineer

Come and See•Colorado Springs, CO

4d•Onsite

About The Position

The Senior Data Engineer / Data Architect is responsible for designing, building, and governing the data infrastructure that powers Come and See Foundation’s analytics and business intelligence capabilities. This senior technical leader owns the architecture of our Snowflake-based data platform, ensures data flows reliably from source to insight, and partners with stakeholders across the organization to build a scalable, secure, and well-governed data ecosystem. Snowflake is the core of our stack, serving as both the data warehouse and the primary transformation layer. The ideal candidate is a seasoned engineer with deep Snowflake expertise, strong architectural instincts, and a passion for using technology in service of a mission-driven organization.

Requirements

Expert-level proficiency in Snowflake, including architecture, performance tuning, Snowpark, native transformation features (tasks, streams, dynamic tables), data sharing, and cost management
Advanced SQL skills with experience in complex data modeling, warehouse design, and query optimization at scale
Strong programming skills in Python for pipeline development, automation, and data engineering tasks
Experience with Airflow or similar orchestration tools for pipeline scheduling and management
Proficiency with cloud platforms (AWS, Azure, or GCP) and cloud-native data services
Experience integrating with CRM platforms such as Virtuous or HubSpot
Familiarity with digital fundraising platforms such as FundraiseUp
Experience with RudderStack or other customer data platforms (CDPs) for event stream ingestion
Experience with Google Analytics data ingestion and integration
Experience with Git/GitHub for version control and collaborative development
Ability to work within an Agile/Scrum environment with 2-week sprint cycles
Architectural Thinking: Designs solutions that are scalable, maintainable, and aligned with long-term organizational goals
Analytical Thinking: Methodical and diligent with outstanding planning and problem-solving capabilities
Communication: Clearly communicates technical concepts to both technical and non-technical audiences
Collaboration: Partners effectively across functional teams including IT, analytics, programs, and executive leadership
Data Evangelist: Enthusiastic about data and committed to building a data-driven culture
Integrity: Handles sensitive donor and constituent data with the highest standards of ethics and transparency
Independence: Self-directed and capable of leading technical work with minimal supervision
High Standards: Holds self and team to excellence in code quality, documentation, and architecture
Adaptability: Thrives in a dynamic environment and adjusts quickly to evolving priorities and technology
Bachelor’s degree in Computer Science, Data Science, Software Engineering, or a related technical field; advanced degree preferred
Minimum 5-7 years of experience in data engineering, data architecture, or a related role
Minimum 2-3 years of hands-on Snowflake experience in a production environment, including native transformation capabilities
Demonstrated experience designing and delivering enterprise-grade data platforms at scale

Nice To Haves

Experience with containerization technologies (Docker, Kubernetes)
Familiarity with Tableau or other BI tools, with ability to support analytics team data requirements
Working knowledge of machine learning workflows, MLOps, and supporting data science teams (XGBoost, LightGBM, scikit-learn)
Experience managing large-scale data migrations or platform transitions
Exposure to data governance frameworks and data cataloging tools
Experience in nonprofit, faith-based, or mission-driven organizations a plus

Responsibilities

Data Architecture & Platform Engineering
Design, implement, and continuously improve the organization’s Snowflake data warehouse architecture, including schema design, data modeling, performance optimization, and native transformation workflows
Own Snowflake as the primary transformation layer, leveraging native features (Snowpark, tasks, streams, dynamic tables) in place of external transformation tooling
Establish data architecture standards, patterns, and best practices across the data platform
Lead the evaluation and selection of new data technologies and tools that align with organizational strategy
Architect and maintain scalable, fault-tolerant data pipelines that reliably process high volumes of structured and unstructured data
Design and implement data models (dimensional, relational, or data vault approaches as appropriate) that support BI and analytics use cases at scale
Data Pipeline & ELT Development
Build, optimize, and maintain robust ELT pipelines to integrate data from Virtuous, FundraiseUp, HubSpot, RudderStack, Google Analytics, and other sources into Snowflake
Develop and maintain data ingestion frameworks using Python and SQL
Implement Airflow-based orchestration for pipeline scheduling, monitoring, alerting, and failure recovery
Manage platform migrations and technology transitions with minimal disruption to downstream analytics
Implement automation for routine data engineering tasks to improve reliability and reduce manual overhead
Cloud Infrastructure & Integration
Manage and optimize cloud data infrastructure on Snowflake, ensuring cost-efficiency, performance, and scalability
Develop and maintain APIs for data access and interoperability across organizational systems
Implement and manage RudderStack event streaming pipelines, ensuring clean, reliable event data flows into Snowflake
Maintain integrations with CRM platforms (Virtuous, HubSpot), fundraising tools (FundraiseUp), and analytics platforms (Google Analytics, Tableau)
Collaborate with IT and engineering partners to define data access protocols, authentication, and authorization patterns
Evaluate and implement containerization (Docker, Kubernetes) strategies for deploying and scaling data engineering applications where appropriate
Data Governance & Quality
Implement and enforce data governance policies, including data classification, lineage tracking, and stewardship across the data lifecycle
Lead data quality assurance efforts, establishing validation frameworks and automated quality checks within pipelines
Enforce role-based access controls and data security protocols to protect sensitive donor and constituent data
Ensure compliance with applicable data privacy regulations (GDPR, CCPA) in all data handling practices
Maintain thorough documentation for data models, pipeline architecture, and operational processes in Git/GitHub
BI Enablement & Stakeholder Partnership
Partner closely with Data Analysts and Data Scientists to ensure the Snowflake platform meets analytical requirements and enables high-quality Tableau reporting
Collaborate with the Director of Data & BI/Analytics, COO, and other senior leaders to align data infrastructure with organizational and ministry strategy
Serve as a key liaison between technical data teams and business stakeholders, translating requirements into sound engineering solutions
Support the Customer 360 intelligence platform, including donor scoring models, self-serve dashboards, and AI-powered analytics infrastructure
Provide technical mentorship to junior data engineering staff and foster a culture of engineering excellence
Evangelize data best practices and data literacy across the organization
Project Management & Continuous Improvement
Manage data engineering projects from design through delivery using Scrum methodology and 2-week sprint cycles
Continuously evaluate and improve data engineering processes, adopting emerging best practices and Snowflake capabilities
Identify cost optimization opportunities within the Snowflake platform and implement efficiency improvements
Maintain version control discipline using Git/GitHub across all pipeline and transformation code