Job Title

• Data Engineer Location
• Glendale, CA (Hybrid
• 2•4 Days Onsite)

Duration:

Full Time Job Summary Mandatory skills Databricks experience (primary requirement) Apache Airflow Advanced SQL skills Python Spark / PySpark Scala Experience building and maintaining data pipelines and workflows

Key Responsibilities:

• Design, write, test, and deploy data pipelines using PySpark, Scala, SQL, Python
• Meet with stakeholders to gather requirements and translate them into scalable data platform solutions
• Understanding of Databricks platform and developer tooling to diagnose errors, audit platform activity, and automate updates across pipelines, objects, and integrations
• Ability to explain Spark architecture and pipeline behavior to stakeholders to diagnose root causes and recommend solutions
• Provide solution architecture across AWS, Databricks, Kubernetes, and Airflow (MWAA), including cross-platform integrations
• Manage Databricks platform governance, including Unity Catalog, ACLs, lineage, and data discovery and privacy tooling
• Build and maintain Kubernetes containers and containerized utilities supporting deployed data platform services
• Apply networking knowledge to troubleshoot connectivity and integration errors across platform components
• Perform platform administration: provision and remove access, assess resource utilization, monitor platform health and cost, and evaluate stakeholder requests
• Collaborate with engineers, architects, and product managers to drive Core Data platform success; participate in agile/scrum ceremonies
• Maintain documentation of platform changes, standards, and pipeline configurations to support data quality and governance

Qualifications:

• 5+ years of data engineering experience 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-executors, stages, partitioning, shuffle, and performance tuning-with 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
• Proficient in SQL with advanced performance tuning capabilities
• Hands-on production experience with Airflow (MWAA) for orchestrating data pipelines
• Experience managing Databricks platform governance: 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 (compute, storage, networking, IAM); experience with other cloud providers is transferable
• Proficiency with Docker and Kubernetes for building and maintaining containerized data platform services
• Working knowledge of networking concepts to diagnose cross-platform integration and connectivity issues
• Familiarity with Snowflake and comparable tooling relative to the Databricks ecosystem
• Experience designing and implementing CI/CD and DevOps practices (Git-based workflows)
• Experience implementing data quality checks, monitoring, and logging for pipeline reliability
• Self-starting problem solver with strong analytical and communication skills; willingness to learn new tooling and trends
• Familiar with Scrum and Agile methodologies
• Experience with Snowflake is a plus
• Bachelor s degree in computer science, Information Systems, or a related field, or equivalent work experience, Master s Degree is a plus.