Role: Sr Data Engineer - Disney
Location: Glendale, CA (Hybrid - 2- 4 Days Onsite)
Full Time
In Person is interview is must
Mandatory skills: Databricks experience (primary requirement), Apache Airflow, Advanced SQL skills, Python, Spark / PySpark, Scala, Experience building and maintaining data pipelines and workflows
Job Description:
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)
