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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.
Impact You Will Make in the Role:
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, and compliance requirements 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
What You Will Bring:
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
Preferred Qualifications/Experience:
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
Skills Required
- 4+ years of data engineering experience
- At least 2 years on Databricks or the Apache Spark ecosystem (Azure and/or AWS)
- Proficiency in PySpark, SQL, and Python with production-grade pipeline experience
- Hands-on experience with Delta Lake, including schema evolution, ACID transactions, optimize/vacuum, incremental and streaming patterns
- Pipeline performance tuning and compute optimization in production Databricks environments
- Working knowledge of PostgreSQL including query optimization and schema design
- Experience supporting and maintaining legacy ETL tooling (SSIS, Informatica, custom Python/SQL) in production
- Experience with large-scale multi-tenant architectures, tenant isolation, per-tenant performance, and data privacy
- Proven ability to collaborate across data science, product, and infrastructure teams and own end-to-end delivery
- Strong understanding of data governance, security, compliance, access control, and protection of sensitive enterprise data
- Willingness to participate in on-call rotation and after-hours incident response
- Experience operating Databricks workspaces across both Azure and AWS, including cost governance and cluster management
- Experience optimizing Databricks workloads in Serverless environments and compute cost governance
- 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)
- Experience with customer onboarding automation or IaC patterns for provisioning tenant data pipelines
- Databricks Certified Data Engineer Associate or Professional
SugarAI Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about SugarAI and has not been reviewed or approved by SugarAI.
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Fair & Transparent Compensation — Pay is often characterized as fair for the role, with multiple statements describing compensation as good or reasonable. Compensation is also framed as competitive for certain technical and senior positions, suggesting stronger alignment with expectations in those segments.
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Healthcare Strength — Healthcare coverage is described as comprehensive, spanning medical, dental, vision, life, disability, FSA/HSA options, and mental health support. Benefits are sometimes characterized as “amazing,” indicating that healthcare and related coverage can be a standout component of the package.
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Wellbeing & Lifestyle Benefits — Wellbeing support appears in the form of mental health programs and wellness-related reimbursements or stipends. Flexibility-related perks (such as remote work support and home-office/tech stipends in some descriptions) further add to the perceived lifestyle value of the overall rewards package.
SugarAI Insights
What We Do
SugarAI provides software that helps revenue teams manage and grow their customer accounts with greater visibility and control. Our Precision Selling platform delivers account-level insights that highlight risk, uncover expansion opportunities, and guide sellers toward clear next steps. Used by thousands of companies in more than 120 countries, SugarAI supports organizations operating in complex, relationship-driven sales environments.
Why Work With Us
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.
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