Senior AI Data Analytics Engineer

BillGO; Fort Collins (CO); On-site; USD 132,800 - 196,500; Full time

Job summary

BillGO in Fort Collins, CO is seeking a Senior AI Data Analytics Engineer to own data models, semantic layers, and AI/RAG patterns that serve Product, Finance, Risk, and Operations. This individual contributor role drives trusted data as the company scales, enabling faster reconciliation and AI-powered insights across teams.

You will architect data solutions across Snowflake, AWS RDS, DynamoDB, and S3, define dictionaries and catalogs, and establish quality and governance standards.

Qualifications

• 5+ years in analytics engineering or data engineering with senior responsibilities.
• Expert SQL and data analytics, able to model complex datasets and design end-to-end data architecture.
• Strong Snowflake data warehousing experience and familiarity with Coalesce transformation frameworks.
• Experience building metrics layers or semantic models used across multiple teams.
• Proficient in ELT pipelines, data orchestration, and Python for data processing.
• Hands-on AI/LLMs applications in production, including RAG and embeddings.
• Track record of technical leadership and mentorship focused on data accuracy and AI outputs.
• Payments/fintech or enterprise SaaS experience preferred.

Responsibilities

• Own architecture and roadmap for scalable data models covering customers, payments, transactions, settlements, and reporting.
• Design data dictionaries, semantic layers, and catalogs powering analytics for humans and AI.
• Establish frameworks for data quality, governance, and testing across the org.
• Partner with Product, Finance, Risk, and Operations to define metrics and enable insights.
• Coach the team on AI tooling, responsible AI practices, and documentation.

Skills

Analytics engineering

SQL

Data modeling

Python

ELT pipelines

LLMs

RAG

Mentorship

Tools

Snowflake

Coalesce

Job description

Job Location: Fort Collins, CO 80528. Position Type: Full Time. Salary Range: $132,800.00 - $196,500.00 per year. Travel Percentage: None. Job Category: Engineering. Title: Senior AI Data Analytics Engineer.

Why This Role Matters

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.

What You’ll Do

Data & AI Architecture

• 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.

Data Quality, Governance & Standards

• 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.

Business Partnership & Enablement

• 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.

Technical Leadership & Mentorship

• 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.

How You’ll Use AI

This role treats AI as core infrastructure, not a side project. You’ll 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. You’ll 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. You’ll design, build, and operationalize AI/ML workflows – from feature engineering to LLM‑powered pipelines – that turn analytics into predictions and automation. And because AI‑generated insight is only as good as its validation, you’ll establish the responsible AI practices – around bias, hallucination, and data privacy – that ensure AI outputs are checked before they influence a financial decision. You’ll also coach the broader team on using AI coding and analytics assistants to work faster and document better.

Qualifications

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

Compensation

• Base salary ($132,800 - $196,500)
• Performance incentive
• Equity opportunities
• Comprehensive health, retirement, and lifestyle benefits