Senior AI Data Analytics Engineer

BillGO, Inc.•Fort Collins, CO

1d•$132,800 - $196,500•Onsite

About The Position

BillGO is building the next generation of payments — an intelligent network that helps small businesses get paid faster, operate leaner, and grow with confidence. The Senior AI Data Analytics Engineer sets the technical direction for BillGO's data and AI architecture, turning payments data into reliable, scalable, and intelligent products the rest of the organization builds on. Reporting to the VP, Data Office, this role sits at the intersection of data engineering, analytics, AI/ML, and the business. It's an individual-contributor role with no direct reports — leadership is exercised through architecture, standards, and mentorship, not people management. Success is measured by the reliability, reuse, and trustworthiness of BillGO's data and AI products, not the volume of models or dashboards produced. BillGO's future runs on trustworthy data, and this role owns making sure it stays that way as the company scales. As the architect of the data models, semantic layers, and AI/RAG patterns that Product, Finance, Risk, and Operations all build on, this person turns scattered payments data into a single source of truth - while setting the validation standards that keep AI-generated insights accurate before they ever reach a decision-maker. It's an individual contributor role with outsized reach: get it right, and BillGO moves faster with more confidence - faster reconciliation, fewer fraud losses, and self-service, AI-powered insight in the hands of every team instead of just a few.

Requirements

5+ years in analytics engineering, data analytics, or data engineering, including senior or lead responsibilities
Expert SQL and data analytics skills, with proven ability to model complex datasets (fact/dimension modeling, star schemas) and design data architecture end to end
Deep experience with data warehousing (Snowflake) and transformation frameworks like Coalesce, including establishing team conventions
Experience building and owning metrics layers or semantic models used across multiple teams
Strong command of ELT pipelines, data orchestration, and Python for data processing and automation
Extensive hands-on experience applying generative AI and LLMs to real data and analytics problems in production
Strong experience with RAG, embeddings, and vector databases, plus a solid ML and MLOps foundation
A track record of technical leadership and mentorship, with a critical eye for data accuracy and AI-generated results
Payments, fintech, financial services, or enterprise SaaS experience strongly preferred
Skill at influencing and communicating with senior technical and non-technical stakeholders

Nice To Haves

advanced data science/ML experience
LLM fine-tuning or benchmarking
agentic AI workflows
event-driven or streaming architectures
hands-on knowledge of payments concepts like authorization/settlement, interchange, chargebacks, and reconciliation.

Responsibilities

Own the architecture and roadmap for scalable data models covering customers, payments, transactions, settlements, and financial reporting
Architect solutions across Snowflake, AWS RDS, and AWS DynamoDB, integrating sources from AWS S3
Lead the design of data dictionaries, semantic layers, and data catalogs that power both human and AI-driven analytics
Set and evangelize engineering standards, patterns, and best practices, and drive their adoption across the organization
Establish frameworks for data quality and integrity through testing, monitoring, and documentation
Support regulatory and financial reporting needs — reconciliation, audit readiness — with accurate, well-governed data
Partner with senior leaders across Product, Finance, Risk, and Operations to define key metrics and enable insights, dashboards, and predictive models
Translate ambiguous business strategy into data and AI solutions that scale with company growth
Put AI-powered, self-service insights in the hands of every team
Set technical direction that improves visibility into payment performance and revenue drivers
Mentor and coach engineers through design reviews, pairing, and code review
Coach the team on using AI coding and analytics assistants to accelerate development and documentation
Apply generative AI and large language models (e.g., Claude) to accelerate data transformation, documentation, and metric definition, and to enable natural-language access to enterprise data.
Architect retrieval-augmented generation (RAG) and semantic search over enterprise data so trusted datasets are easily discoverable and queryable by both humans and AI systems.
Design, build, and operationalize AI/ML workflows — from feature engineering to LLM-powered pipelines — that turn analytics into predictions and automation.
Establish the responsible AI practices — around bias, hallucination, and data privacy — that ensure AI outputs are checked before they influence a financial decision.
Coach the broader team on using AI coding and analytics assistants to work faster and document better.