Senior Data Engineer

The Senior Data Engineer will support enterprise data engineering initiatives by designing, building, and optimizing scalable data solutions. The role focuses on operating the Core Data platform, delivering batch and streaming Spark pipelines with governance across AWS, Kubernetes, and Airflow (MWAA).

Core Responsibilities

• Manage Databricks platform governance, including Unity Catalog, ACLs, lineage, and data discovery and privacy tooling
• Design, develop, test, and deploy batch and streaming data pipelines using PySpark, Scala, SQL, Python, and Kotlin
• Partner with stakeholders to gather requirements and translate them into scalable data platform solutions
• Use Databricks platform and developer tooling to diagnose errors, audit platform activity, and automate updates across pipelines, objects, and integrations
• Explain Spark architecture and pipeline behavior to troubleshoot root causes and recommend solutions
• Provide solution architecture across AWS, Databricks, Kubernetes, and Airflow (MWAA), including cross-platform integrations
• Build and maintain Kubernetes containers and containerized utilities that support deployed data platform services
• Apply networking knowledge to troubleshoot connectivity and integration errors across platform components
• Perform platform administration such as provisioning and removing access, assessing resource utilization, monitoring platform health and cost, and evaluating stakeholder requests
• Collaborate with engineers, architects, and product managers to drive Core Data platform success and participate in agile or scrum ceremonies
• Maintain documentation for platform changes, standards, and pipeline configurations to support data quality and governance
• Develop and maintain scalable data engineering solutions, including building and enhancing data pipelines and workflows
• Support cloud-based data platform initiatives, collaborate on system architecture and design, and implement infrastructure automation and deployment standards
• Troubleshoot and optimize data processing environments

Required Qualifications

• Strong experience with Databricks
• Advanced Python development skills
• Experience with Terraform and Infrastructure as Code
• 5+ years of relevant data engineering experience
• Ability to participate in architecture and system design discussions
• Strong problem-solving and data platform implementation experience

Additional Qualifications and Skills

• 5+ years developing and operating large-scale data pipelines
• Deep hands-on experience with Databricks and Apache Spark (batch and streaming), including pipeline development in PySpark and/or Scala
• Strong understanding of Spark architecture components including executors, stages, partitioning, shuffle, and performance tuning, with the ability to explain tradeoffs to technical and non-technical stakeholders
• Proficiency with Databricks platform tooling (API, SDK, CLI) for automation, auditing, governance, and operational troubleshooting
• Advanced SQL performance tuning capabilities
• Hands-on production experience with Airflow (MWAA) for orchestrating data pipelines
• Experience managing Databricks governance including ACLs, Unity Catalog, lineage, and access provisioning
• Proficiency in Python and at least one additional language: Scala, Kotlin, or SQL-driven pipeline tooling
• Experience designing and optimizing scalable ETL/ELT pipelines integrating diverse structured and unstructured data sources
• AWS-primary experience covering compute, storage, networking, and IAM (transferable experience with other cloud providers is acceptable)
• Proficiency with Docker and Kubernetes for building and maintaining containerized data platform services
• Working knowledge of networking concepts for diagnosing cross-platform integration and connectivity issues
• Familiarity with Snowflake and comparable tooling relative to the Databricks ecosystem
• Experience implementing CI/CD and DevOps practices using Git-based workflows
• Experience implementing data quality checks, monitoring, and logging for pipeline reliability
• Comfort as a self-starting problem solver with strong analytical and communication skills and willingness to learn new tooling and trends
• Familiarity with Scrum and Agile methodologies
• Bachelor’s Degree in Computer Science, Information Systems, or a related field, or equivalent work experience; Master’s Degree is a plus

Nice-to-Have

• Experience with AWS
• Cloud-based data platform experience
• Data pipeline optimization and automation expertise

Technologies

• Databricks, Python, Terraform, AWS, Kubernetes, Airflow (MWAA)
• PySpark, Scala, SQL, Kotlin
• Unity Catalog, Apache Spark
• Docker, Git-based workflows, CI/CD

Location and Work Type

• Glendale, CA (hybrid)

Compensation and Contract Details

• Pay rate: USD $90–$93 per hour (DOE)
• Duration: 12+ months

Benefits

• Medical, dental, and vision coverage
• 401(k) with company match
• Short-term disability
• Life insurance with AD&D

Minimum Education

• BA/BS