Senior Data Engineer - Databricks

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 

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