Lead Software Engineer - DataBricks, Spark, Terraform

We use AI-assisted development as part of our day-to-day workflow, including GitHub Copilot for coding and native Databricks tools such as Databricks SQL Assistant and Genie to accelerate development, troubleshooting, and self-service analytics—while maintaining strong engineering controls and review practices.

Job responsibilities

• Platform leadership & architecture — Define and drive the technical roadmap for our Databricks lakehouse/database platform (ingestion, storage, modeling, serving) with clear standards and reference patterns. 
• Data modeling & database engineering — Design curated datasets (e.g., medallion architecture) using Delta Lake, dimensional/semantic modeling where appropriate, and enforce consistent naming, partitioning, and performance practices. 
• Reliability & operations — Build for availability and predictable performance; establish SLOs, runbooks, alerting, incident response, and operational hygiene for pipelines and SQL workloads. 
• Security, governance & access controls — Implement and maintain strong governance (e.g., Unity Catalog), least-privilege access, auditing, data classification, and lifecycle management. 
• Performance & cost management — Tune Spark/SQL workloads, optimize clusters/warehouses, manage caching and storage patterns, and implement cost observability/chargeback as needed. 
• Engineering excellence — Set standards for code quality, testing, CI/CD, branching strategy, documentation, and review. Establish reusable libraries/templates and enforce consistency across teams. 
• Mentorship & collaboration — Coach engineers, lead design reviews, and partner with stakeholders to translate business needs into scalable data platform capabilities.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

• 7+ years in data engineering/platform engineering, with hands-on Databricks experience in production environments. 
• Strong proficiency in Spark (PySpark/Scala) and SQL, including performance tuning and troubleshooting. 
• Proven experience building and operating data platforms: batch/stream ingestion, transformation frameworks, orchestration, and curated data layers. 
• Experience with Data Lake, (DataBricks, SnowFlake, or AWS) table design, and optimization (partitioning, Z-ORDER, file sizing). 
• Familiarity with Unity Catalog (or equivalent governance tooling): permissions, catalogs/schemas, lineage/auditing concepts. 
• Solid software engineering fundamentals: Git, CI/CD, automated testing, code reviews, modular design, and documentation. 
• Experience implementing observability (logs/metrics/traces), data quality checks, and monitoring for pipelines and SQL workloads. 
• Strong communication skills and demonstrated ability to lead technical decisions across teams.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

• Databricks features: Workflows, Delta Live Tables (DLT), Structured Streaming, Databricks SQL Warehouses. 
• Transformation frameworks (e.g., dbt) and semantic layer patterns. 
• Infrastructure-as-code (e.g., Terraform) and automated environment provisioning. 
• Experience with regulated-data environments, privacy controls, and enterprise data governance programs.