SugarAI is redefining CRM for the age of AI.

We’re delivering on the original promise of CRM—turning fragmented customer and revenue signals into clear, prioritized action. Instead of more dashboards or surface-level insights, we help teams focus on what matters most and know exactly what to do next.

More than two decades after our founding, we’re entering a new chapter with clarity and momentum—building intelligent, intuitive solutions that work within the flow of how teams actually sell and serve. We’re focused on solving complex, real-world challenges where relationships, context, and precision make all the difference.

Our global team is united by a shared commitment to impact, ownership, and continuous growth. We create an environment where thoughtful ideas move quickly, where people are trusted to lead, and where flexibility supports how great work gets done.

If you’re excited to help shape what’s next in AI-driven CRM—and build technology that drives real outcomes—we’d love to meet you.

Where You Fit In:

The Sugar Predict platform powers revenue intelligence for mid-market enterprises by fusing ERP and CRM data into actionable insights. As a Senior Data Engineer, you will own the Databricks pipelines that make this possible, driving production reliability, cost efficiency, and platform growth through customer onboarding and legacy modernization. You will work closely with ML engineers, product teams, and the Enterprise Architecture team to ensure the data backbone behind Sugar Predict is always fast, clean, and ready to deliver at a global scale.

This role operates on a hybrid model, with a mix of remote work and in-office collaboration at our Denver, CO location, specifically, working in-office a minimum of 2-3 days per week.

**We are requiring an in-person interview at our downtown Denver office as a part of the hiring process for this role.**

*NOTE: We are not able to offer Visa Sponsorship at this time*

• 4+ years of data engineering experience

• At least 2 years on Databricks or the Apache Spark ecosystem across Azure and/or AWS

• Proficiency in PySpark, SQL, and Python with a strong track record building and operating production-grade pipelines under SLA constraints

• Hands-on experience with Delta Lake including schema evolution, ACID transactions, optimize/vacuum lifecycle, and both incremental and streaming processing patterns

• Hands-on experience with pipeline performance tuning and compute optimization in production Databricks environments

• Solid working knowledge of PostgreSQL including query optimization, schema design, and use as a source or sink in production data pipelines

• Experience supporting and maintaining legacy ETL tooling (SSIS, Informatica, custom Python/SQL pipelines, or similar) in production

• Experience supporting large-scale multi-tenant architectures with a focus on tenant isolation, per-tenant performance, and data privacy, including navigating tools and platforms that default to single-tenant assumptions

• Proven ability to work collaboratively across data science, product, and infrastructure teams, owning end-to-end delivery in a cross-functional environment

• Strong understanding of data governance, security, and compliance principles, including access control, data privacy, and protection of sensitive enterprise data across multi-tenant environments

• Experience operating Databricks workspaces across both Azure and AWS, including cost governance, cluster management, and cross-cloud data access

• Experience optimizing Databricks workloads in a Serverless environment, including compute cost governance and performance tuning for serverless compute

• Experience with Microsoft SQL Server in a data engineering or ETL context

• Exposure to ML feature engineering or feature stores (Databricks Feature Store, Feast, or similar) supporting predictive analytics

• Experience with customer onboarding automation or IaC patterns for provisioning tenant data pipelines at scale

• Databricks Certified Data Engineer Associate or Professional certification