Technical Solutions Director - Spark/ML/AI

Mission

The Director – Spark/ML/AI, Technical Solutions & Support will be responsible for building and leading a regional team of technical experts in Brazil, focused on resolving highly complex and long-running support cases raised by Databricks customers. This leader will oversee all Support Engineering operations during AMER/LATAM business hours with close alignment to global teams and will ensure 24x7 support coverage through coordination with other regions.

Key Outcomes

• Build and manage a high-performing regional team of Spark, ML, and AI Technical Solution Engineers in São Paulo (or another major Brazil hub).
• Hire, retain, and develop top talent, building a diverse, world-class support engineering organization.
• Coach and mentor regional support managers and future leaders, while driving structured training, technical workshops, and knowledge-sharing initiatives.
• Define and track quarterly goals for team growth, individual development, and overall performance excellence.
• Partner with Engineering and Product teams to improve product supportability by embedding diagnostics, observability, and support-first practices into design and delivery.
• Lead and resolve escalations during LATAM business hours, ensuring cross-functional collaboration with Engineering, Field, and Global Support teams.
• Act as a player-coach – provide technical leadership (deep dives, debugging, RCA) while scaling organizational processes, tools, and guidelines.
• Drive root cause analysis (RCA) and developer-owned quality practices, ensuring issues are permanently fixed, testing and instrumentation are embedded in the lifecycle, and releases are reliable without reliance on “hero fixes.”
• Build strong engineering-support partnerships by aligning roadmaps, sharing visibility into changes, and implementing joint mechanisms (on-call rotations, incident reviews, escalation playbooks) to improve case routing and resolution speed.
• Lead support tooling and automation initiatives (e.g., log parsers, JVM/heap/thread dump analyzers, AI-assisted triage) to accelerate diagnosis and reduce time-to-resolution.
• Collaborate with Field engineering, Sales and Customer Success teams to address account-level concerns and strengthen adoption of the Databricks platform.
• Demonstrate strong ownership, collaboration, and communication skills to build trust with customers and internal stakeholders.
• Participate in global on-call rotations for critical support escalations.

Competencies & Requirements

• Proven people leadership experience: at least 2+ years as a manager of managers.
• 12+ years in the IT industry, with a strong background in Software Engineering, SaaS Support, Data Engineering, or Cloud Platforms.
• Experience leading large teams (50+ employees) in technical support, engineering, or big data consulting.
• Hands-on experience in at least two of the following at production scale:
• Big Data (Spark, Hadoop, Kafka)
• Machine Learning / Artificial Intelligence projects
• Data Science / Streaming use cases
• Spark expertise is a big advantage.
• Strong background in customer-facing support leadership roles.
• Excellent troubleshooting skills across distributed systems.
• Fluent in English and Portuguese (Spanish a plus).
• Strong ownership mindset with the ability to thrive in a fast-paced, startup-like environment.
• Bachelor’s/Master’s in Computer Science or equivalent technical field.

Additional technical expertise (Preferred)

• Strong Java/Scala development, OOP, and distributed systems debugging (JVM, GC, Linux).
• Proficiency in data structures, algorithms, and performance optimization.
• Hands-on with Spark (PySpark, Scala, SQL, Streaming, Performance Tuning, Architecture).
• Experience in data pipeline development & production deployments (Databricks, EMR, On-Prem).
• ML/AI project development and deployment at scale.
• Familiarity with Big Data ecosystems (Hadoop, Hive, Kafka) and major cloud platforms (AWS, Azure, GCP).
• Knowledge of scalable system design, CI/CD practices, and modern DevOps tooling