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# Databricks Lead

- **Company:** SFE PARTNERS LLC
- **Location:** Denver, CO, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Application Frameworks, Computer Programming, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), DevOps, Python (Programming Language), Machine Learning, Meta-Data Management, Cloud Services, DataOps, SQL Databases, Data Streaming, Apache Spark, Event Driven Architecture, Containerization, Data Lakes, Pyspark, Machine Learning Operations, Data Pipelines, Databricks
- **Published:** October 22, 2026
- **Apply:** https://www.careerjet.com/jobad/us4253857151b69fd286fb7262faaefe54

## About the Role

* 12+ years of experience in Data Engineering and Analytics.
 * 5+ years of hands-on Databricks experience.
 * 3+ years leading enterprise-scale data engineering teams.
 * Proven experience building large-scale cloud data platforms.
 * Experience leading distributed teams in Agile delivery environments

## Description

* Databricks Lakehouse Platform
 * Delta Lake
 * Unity Catalog
 * Databricks Workflows
 * Databricks SQL
 * Delta Live Tables
 * MLflow
 * Apache Spark
 * PySpark
 * Data Modeling
 * ETL/ELT Architecture
 * Streaming Data Processing
 * Data Quality Frameworks
 * Event-Driven Architectures

Programming

 * Python
 * SQL

Roles & Responsibilities

 * Define and execute the enterprise data platform strategy leveraging Databricks and cloud-native technologies.
 * Establish reference architectures, standards, and engineering best practices.
 * Lead technical design reviews and architecture governance forums.
 * Create reusable frameworks and accelerators to improve productivity and quality.
 * Design and oversee implementation of scalable batch, streaming, and real-time data pipelines.
 * Lead development of data products supporting analytics, reporting, data science, and AI initiatives.
 * Ensure high standards for reliability, scalability, observability, security, and maintainability.
 * Drive adoption of DataOps, DevOps, and CI/CD practices across engineering teams.
 * Define enterprise Lakehouse architecture patterns.
 * Establish data models, storage strategies, and data lifecycle management processes.
 * Lead implementation of data governance, metadata management, lineage, and access controls.
 * Ensure platform compliance with organizational security and regulatory requirements.
 * Partner with data science and AI teams to operationalize machine learning workloads.
 * Enable model training, deployment, monitoring, and lifecycle management.
 * Support GenAI, predictive analytics, and intelligent automation initiatives.
 * Build scalable data foundations for AI-driven business capabilities.
 * Lead and mentor data engineering teams.
 * Manage technical delivery across multiple workstreams and vendors.
 * Collaborate with business stakeholders to translate requirements into scalable solutions.
 * Provide regular updates to leadership on delivery progress, risks, and architectural decisions.