Sr. Databricks Platform Engineer [Platform Admin] - AWS/Azure/Google Cloud Platform - Dallas TX - Onsite work

Sr. Databricks Platform Engineer [Platform Admin] - AWS/Azure/Google Cloud Platform - Dallas TX - Onsite work

Requisition Name: Sr. Databricks Platform Engineer [Platform Admin]

Start Date: 11/9/2026

Duration: 20 Weeks

Services Location: TX/Dallas

Description Of Services:
Role Summary

We are looking for a highly skilled Sr. Databricks Platform Engineer [Platform Admin] to lead the design and implementation of scalable, enterprise-grade data platforms using Databricks. The ideal candidate will combine strong technical expertise in data engineering and cloud platforms (AWS/Azure/Google Cloud Platform) with architectural leadership, solution design capability, and strong stakeholder engagement skills.

Required Skills & Experience

Technical Skills

Databricks Expertise: Strong hands-on experience with Databricks (clusters, notebooks, Delta Lake, MLflow, Unity Catalog).

Cloud Platforms: Experience with at least one cloud provider (AWS, Azure, Google Cloud Platform).

Data Engineering: Strong proficiency in Spark, Python, SQL, and distributed data processing.

Architecture: Experience designing large-scale data solutions including ingestion, transformation, storage, and analytics.

Streaming: Experience with streaming technologies (Structured Streaming, Kafka, Kinesis, EventHub).

DevOps: CI/CD practices for data pipelines (Azure DevOps, GitHub Actions, Jenkins, etc.).

Soft Skills

Strong communication skills with the ability to engage both technical and business teams.

Experience working in Agile environments.

Ability to simplify complex technical concepts for non-technical audiences.

Strong analytical, problem-solving, and decision-making abilities.

Preferred Qualifications

Databricks Certified Data Engineer Professional / Architect certification.

AWS/Azure/Google Cloud Platform cloud architect certifications.

Experience with BI tools (Tableau, Power BI, Looker).

Experience in machine learning workflows and ML operations.

Background in large-scale data modernization or cloud migration projects.

Why Join Us?

Opportunity to lead high-impact data initiatives using cutting-edge Databricks capabilities.

Work with a highly skilled team of engineers, architects, and analytics professionals.

Professional growth opportunities including certifications and advanced architecture training.

Collaborative environment that values innovation and continuous improvement.

Deliverables:
1. Solution Architecture & Design Design end-to-end data architectures using Databricks Lakehouse Platform. Architect scalable ETL/ELT pipelines, real-time streaming solutions, and advanced analytics platforms. Define data models, storage strategies, and integration patterns aligned with business and enterprise architecture standards. Provide guidance on cluster configuration, performance optimization, cost management, and workspace governance.

2. Technical Leadership Lead technical discussions and design workshops with engineering teams and business stakeholders. Provide best practices, frameworks, and reusable component designs for consistent delivery. Perform code reviews and provide technical mentoring to data engineers and developers.

3. Stakeholder & Project Engagement Collaborate with product owners, business leaders, and analytics teams to translate business requirements into scalable technical solutions. Create and present solution proposals, architectural diagrams, and implementation strategies. Support pre-sales or discovery phases with technical input when needed.

4. Data Governance, Security & Compliance Define and implement governance standards across Databricks workspaces (data lineage, cataloging, access control, etc.). Ensure compliance with regulatory and organizational security frameworks. Implement best practices for monitoring, auditing, and data quality management.

5. Continuous Improvement & Innovation Stay updated on Databricks features, roadmap, and industry trends. Recommend improvements, optimizations, and modernization opportunities across the data ecosystem. Evaluate integration of complementary technologies (Delta Live Tables, MLflow, Unity Catalog, streaming frameworks, etc.).