Senior Data Engineer - Databricks

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