Want to know if this job is worth applying to?
Denver, Colorado, United States
Hybrid
Not Available
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*
Own Databricks production support for the Sugar Predict data platform, including monitoring, alerting, and incident response across all production data flows
Maintain and report on SLA performance metrics for data pipeline delivery, ensuring visibility into platform health and accountability across internal and external stakeholders
Identify and implement pipeline optimizations that reduce Databricks compute costs, improve throughput, and reduce processing windows while tracking impacts through measurable KPIs
Migrate legacy ETL/ELT pipelines to Databricks, building automation tooling to reduce manual intervention and ensure uninterrupted data delivery during transitions
Support new customers onboarding by provisioning, validating, and hardening tenant data pipelines that deliver reliable, isolated data from day one
Design and build high-performance Databricks pipelines that ingest, transform, and serve ERP and CRM data at scale across both Azure and AWS environments
Own the Delta Lake architecture including schema design, partitioning strategies, data quality enforcement, and incremental processing patterns
Enforce data security best practices across Databricks environments, including role-based access control, secrets management, Unity Catalog Isolation Attestation Maps, and compliance controls for enterprise CRM and ERP data.
Implement data quality monitoring and observability across pipeline health and ML model inputs, ensuring data integrity that directly supports Sugar Predict prediction accuracy
Apply and enforce multi-tenant data isolation patterns ensuring reliable, secure data delivery across Sugar Predict enterprise customers
Partner with the Enterprise Architecture team to ensure Sugar Predict data pipelines integrate seamlessly with the broader SugarAI product ecosystem
Support a globally distributed operation through on-call rotation and after-hours incident response, meeting SLAs across multiple time zones
Maintain technical documentation, runbooks, and architectural decision records, contributing to team knowledge sharing and operational readiness across on-call and incident response scenarios
Apply CI/CD best practices to data pipeline development, including version control, automated testing, and deployment tooling to ensure reliable and repeatable pipeline delivery
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
