Staff Cloud Data Engineer

Job Description

This role is categorized as hybrid. This means the successful candidate is expected to report onsite at the Austin, TX, Warren, MI, or Roswell, GA three times per week, at minimum or other frequency dictated by the business.

The Role

As a Staff Cloud Data Engineer, you will play a critical role in architecting, designing, and delivering scalable, high-performance data solutions in the cloud. You will lead the development of systems that support efficient data processing, storage, and retrieval. This is a senior-level role that requires deep technical expertise, strong leadership, and a demonstrated history of executing complex data engineering initiatives.

In addition to strong data engineering capabilities, a solid foundation in software engineering principles—such as code quality, design patterns, testing, and CI/CD—is highly valued. The ideal candidate combines a data-driven mindset with modern software engineering best practices to build robust, maintainable, and production-ready data systems.

Prospective team member possesses a high degree of business insight, creativity, decision making skills, a drive for results, the ability to negotiate, the ability to develop strong peer relationships, and a strong technical learning capability and focus.

Your Skills & Abilities (Required Qualifications)

• Bachelor’s Degree in Computer Science, Engineering, or equivalent degree
• Over 10 years of experience in building, operating scalable and reliable platforms.
• Expertise leading Agile (scrum and feature driven development) teams that have regularly (daily + weekly) delivered software while practicing code reviews
• Develop data models and schemas that support efficient data storage, retrieval, and analytics, employing optimization techniques to enhance query performance and scalability
• Expertise in SQL (relational databases), key-value datastores, and document stores
• Creating self-contained, reusable, and testable modules and components in frontend and backend work
• Leverage big data technologies and frameworks (e.g., Hadoop, Spark, Hive) to process and analyze large volumes of data, enabling advanced analytics and machine learning initiatives.
• Manage and optimize data infrastructure, including cloud-based platforms, containerization technologies, and distributed computing environments.
• Ensure the security and privacy of our data and compliance with relevant regulations.
• Evaluate new technologies and tools for data processing, storage, and retrieval and recommend solutions to improve the efficiency and scalability of our data infrastructure.
• Strong proficiency in data engineering technologies, such as ETL frameworks, big data processing, and SQL and NoSQL databases.
• Excellent verbal and written communication skills and ability to effectively communicate and translate feedback, needs and solutions
• Creative problem-solving skills that deliver elegant solutions to complex issues
• Strong understanding of distributed systems and the modern data stack
• Experience with Databricks or snowflake and Azure/GCP platforms
• Experience using Git source control doing rebases, merges, and handling merge conflicts
• Experience in enterprise integration, common integration patterns (batch, micro-batch, near real-time and real time) and ETL tools
• Knowledge of cloud-native architecture and best practices
• Demonstrated knowledge and implementation experience of Data Streaming architectures
• Define, document, and maintain architecture patterns

Additional Job Description

What Can Give You a Competitive Advantage (Preferred Qualifications)

• Over 6 years utilizing platform and infrastructure as a service technologies and capabilities and their corresponding services (object store, configuration management, service registries, etc)
• Experience with Databricks/Snowflake and Azure/GCP platforms will be an added advantage
• Experience using Git source control doing rebases, merges, and handling merge conflicts
• Exposure to software defined networking, zero trust security models, micro segmentation and second layer of defense technologies
• Experience in enterprise integration, common integration patterns (batch, micro-batch, near real-time and real time) and ETL tools
• Working knowledge of Hadoop, Spark, Object Storage (ADLS/S3), Event Queues
• Demonstrated knowledge and implementation experience of Data Streaming architectures and design principles
• Knowledge of cloud-native architecture and best practices
• Hands on Experience with stream processing in Kubernetes
• Hands on experience with E