Databricks Forward Deployed Engineer

CLOUDTECH INNOVATIONS LLC

Euless, TX, United States

about 1 month ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Airflow Amazon Web Services Computing Platforms Automation of Tests Microsoft Azure Cloud Computing Continuous Delivery Continuous Integration Information Engineering Data Governance Extract Transform Load (ETL) Data Systems

+26 more

Software Debugging DevOps Github Node.Js Performance Tuning Query Optimization Role-Based Access Control SQL Databases Data Streaming Systems Integration Google Cloud Enterprise Software Applications Flask (Web Framework) Delivery Pipeline Apache Spark Fastapi Containerization Pyspark Apache Kafka Data Management Terraform Software Version Control Serverless Computing Docker Jenkins Databricks

Job description

We are seeking an experienced Forward Deployed Engineer (FDE) to act as the primary technical bridge between our data platforms and client success. This is a high-velocity role for an engineer who thrives in ambiguity and can context-switch seamlessly between multiple concurrent client projects. Unlike traditional architecture roles, you will be embedded directly within client delivery teams to own the end-to-end implementation of Databricks-centric Lakehouse platforms. You will be responsible for translating complex requirements into production-ready systems, ensuring rapid delivery without compromising technical rigor., * Rapid Delivery & Multi-Project Agility: Manage technical priorities across multiple client engagements simultaneously, pivoting between distinct architectural needs in a fast-paced, high-demand environment.

CI/CD & Engineering Rigor: Architect and maintain automated CI/CD pipelines (GitHub Actions, Azure DevOps, Jenkins) for data engineering workflows, ensuring robust version control, automated testing, and seamless deployment of Databricks assets.
Embedded Solution Delivery: Act as a core member of client delivery teams, translating business challenges into functional, high-impact data solutions. Platform Architecture: Architect, build, and launch an end-to-end Databricks Lakehouse platform using the Medallion Architecture (Bronze/Silver/Gold).
Engineering Ownership: Own the full development lifecycle, from technical design and ETL/ELT pipeline construction (Databricks Workflows, SQL Warehouses, Spark) to deployment, monitoring, and production support.
Data Governance & Security: Implement governed Lakehouse patterns (Unity Catalog, RBAC/ABAC, lineage, compliance) while balancing security with development agility.
System Integration: Design distributed integration patterns across cloud-native services (AWS, Azure, Google Cloud Platform) and enterprise systems (CRM, ERP, Kafka/Kinesis streams).
Operational Excellence: Lead architecture reviews, mentor client engineering teams, and drive cloud cost optimization across storage and compute.

Requirements

Deep Databricks Expertise: Strong hands-on experience with Unity Catalog, Delta Live Tables (DL T), Photon engine, and Lakehouse Federation. CI/CD & DevOps: Demonstrated experience implementing CI/CD pipelines for data platforms. Proficiency with Infrastructure as Code (Terraform) and version control workflows.
Data Engineering Rigor: Proficiency in Spark (PySpark & Scala), complex query tuning, performance optimization, and pipeline automation (Airflow, dbt).
Full-Stack & Cloud Mindset: Hands-on experience with cloud infrastructure (AWS/Azure/Google Cloud Platform), containerization (Docker), and building/debugging data-connected applications (FastAPI/Flask/Node.js).
Streaming & Real-time: Experience with real-time streaming technologies (Kafka, Kinesis, Pub/Sub, Auto Loader).