RSA (Senior Forward Deployed Engineer – Databricks)

Prabhav Services Inc; United States; Remote; USD 124,000 - 207,000; Part time

Job summary

Prabhav Services Inc. seeks a Senior Forward Deployed Engineer specializing in Databricks to lead end-to-end data engineering implementations in customer environments.

The role combines hands-on engineering, solution architecture, systems integration, and customer-facing leadership. The candidate will translate complex requirements into scalable technical solutions, own implementation, deployment, and delivery outcomes, and collaborate with stakeholders to ensure success across cloud-native data

Qualifications

• 12 years of professional software/data engineering or related roles.
• Strong hands-on Databricks experience for enterprise-scale solutions.
• Expertise in Apache Spark, PySpark, Python, and SQL.

Responsibilities

• Lead end-to-end Databricks implementations, including data engineering and solution architecture.
• Design, build, deploy production-grade data pipelines on Databricks, Spark, PySpark, Python and SQL.
• Build scalable cloud-native data solutions on AWS, Azure, or GCP.
• Design Lakehouse architectures and enterprise data engineering solutions.
• Translate business requirements into technical designs and reusable patterns.
• Integrate Databricks with enterprise systems and data platforms.
• Troubleshoot performance issues and optimize pipelines and Spark workloads.
• Implement CI/CD, automated deployments, and MLOps where applicable.
• Collaborate with customers and cross-functional teams to ensure delivery.

Skills

Databricks

Apache Spark

PySpark

Python

SQL

AWS/Azure/GCP

CI/CD

MLOps

Job description

Experience: 12 Years

Work Location: Remote

Employment Type: Contract

Role Overview

We are seeking an experienced Senior Forward Deployed Engineer specializing in Databricks to lead end-to-end data engineering implementations and deliver scalable, production-ready solutions in customer environments. The ideal candidate will combine strong hands-on engineering expertise with solution architecture, systems integration, technical consulting, and customer-facing leadership.

The candidate should be comfortable translating complex and ambiguous business requirements into scalable technical solutions while taking ownership of implementation, deployment, troubleshooting, and delivery outcomes.

Key Responsibilities

• Lead end-to-end Databricks implementations, including data engineering, systems integration, application integration, and solution architecture.
• Design, develop, deploy, and maintain production-grade data pipelines using Databricks, Apache Spark, PySpark, Python, and SQL.
• Build scalable, cloud-native data solutions using AWS, Azure, or Google Cloud Platform.
• Design and implement modern Lakehouse architectures and enterprise-grade data engineering solutions.
• Translate business requirements into technical designs, reusable implementation patterns, and scalable solutions.
• Integrate Databricks with existing enterprise systems, applications, and data platforms.
• Troubleshoot complex technical issues and optimize data pipelines, Spark workloads, query performance, and overall system efficiency.
• Implement CI/CD practices, automated deployment workflows, and MLOps capabilities where applicable.
• Collaborate directly with customers, business stakeholders, and cross-functional engineering teams to ensure successful project delivery.
• Provide technical consulting, architectural guidance, and hands-on engineering support throughout the implementation lifecycle.
• Communicate technical concepts effectively to both technical and non-technical stakeholders.
• Take ownership of delivery outcomes, build customer trust, and ensure solutions meet business and technical requirements.

Required Qualifications

• 12 years of professional experience in software engineering, data engineering, solution architecture, or related technical roles.
• Strong hands-on experience implementing enterprise-scale solutions using Databricks.
• Expertise in Apache Spark, PySpark, Python, and SQL.
• Experience designing and implementing data pipelines and modern Lakehouse architectures.
• Strong knowledge of at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (Google Cloud Platform).
• Experience with systems integration, solution design, and enterprise data architecture.
• Knowledge of CI/CD, performance optimization, and production deployment practices.
• Strong troubleshooting, analytical, and problem-solving skills.
• Excellent communication, stakeholder management, and customer-facing consulting skills.
• Demonstrated ability to work independently, manage technical challenges, and take ownership of end-to-end delivery.

Preferred Qualifications

• Databricks Certified Data Engineer Professional certification.
• Experience with MLOps, workflow orchestration, and automated data deployment.
• Experience delivering complex Databricks implementations in customer-facing environments.
• Ability to develop reusable architectural patterns and technical best practices.
• Experience leading technical discussions, solution demonstrations, and implementation workshops.

Ideal Candidate Profile

The ideal candidate is a hands-on technical leader who can operate at the intersection of engineering, architecture, and customer consulting. They should be comfortable working in fast-paced environments, navigating ambiguous requirements, solving complex technical problems, and representing engineering teams confidently in front of customer stakeholders.

Core Technical Skills: Databricks, Apache Spark, PySpark, Python, SQL, AWS/Azure/Google Cloud Platform, Data Engineering, Lakehouse Architecture, Systems Integration, Solution Architecture, CI/CD, Performance Optimization, and MLOps.