Senior Data Engineer

Workplace

• Must be willing to work hybrid from Glendale, California.
• The listed work location is hybrid remote in Glendale, CA 91201.

Role summary

Responsibilities

• Design data models and scalable ETL/ELT pipelines for structured and unstructured sources.
• Develop and operate batch and streaming pipelines using Databricks, Spark, and PySpark or Scala.
• Tune Spark performance and SQL; explain tradeoffs to technical and non-technical stakeholders.
• Use Databricks API, SDK, and CLI for automation, auditing, governance, and operational troubleshooting.
• Manage ACLs, Unity Catalog, lineage, and access provisioning.
• Orchestrate pipelines with Airflow (MWAA); implement data quality checks, monitoring, and logging.
• Use AWS infrastructure and Docker and Kubernetes for containerized platform services.
• Apply CI/CD and DevOps practices, Git-based workflows, and networking knowledge to resolve integration issues.

Qualifications

• 8+ years of data engineering experience developing and operating large-scale data pipelines.
• Hands-on Databricks and Apache Spark experience across batch and streaming, including PySpark and/or Scala.
• Strong Spark architecture and SQL performance-tuning knowledge; proficiency with Databricks platform tooling.
• Production Airflow (MWAA) experience and Databricks governance experience.
• Proficiency in Python and at least one additional language: Scala, Kotlin, or SQL-driven pipeline tooling.
• Experience with scalable ETL/ELT, AWS, Docker, Kubernetes, CI/CD, data quality, monitoring, and logging.
• Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent work experience; a master’s degree is a plus.
• Snowflake experience is a plus; experience with other cloud providers is transferable.

Pay

• Pay is $142,277.97–$171,345.52 per year.

Benefits

• Benefits include 401(k), dental insurance, flexible schedule, and parental leave.

Additional qualifications