Data Engineer

Position: Data Engineer
Location: Santa Monica, CA, Burbank, or Glendale (USA), (Hybrid, 4 days onsite; 1 day remote)
Employment Type: Full-Time
Experience Required: 5 Years to 10 years
Work Authorization: Must have Valid USA Work permit
Annual Compensation: $90,000–$1,20,000, depending on qualifications and experience.

*Benefits:*

Health insurance: Medical, dental, and vision.
401(k) retirement plan (no employer matching contribution).
Paid vacation/PTO, subject to the employer's policy.
Paid sick leave in accordance with applicable Washington State law.
Other applicable employee benefits as provided by company policy.

Key Responsibilities
● Assist in building and maintaining ETL/ELT pipelines for acquisition reporting using Databricks, PySpark, SQL, and Unity Catalog under the guidance of senior engineers.
● Support the migration of existing Snowflake SQL scripts and transformations into Databricks UC by updating queries, validating outputs, and helping implement governance best practices.
● Contribute to developing ingestion processes for marketing vendor data, including data parsing, normalization, and basic quality validations.
● Implement and maintain foundational data quality checks, monitoring alerts, and issue triage workflows using Databricks, Snowflake, Airflow, and internal tooling.
● Partner with the Data Reliability Engineering team to assist with SLA monitoring, simple incident troubleshooting, and logging improvements.
● Collaborate with analytics and marketing partners to understand data requirements and ensure accuracy of datasets used in dashboards and reporting.
● Support performance tuning, logging improvements, and general pipeline reliability work.
● Participate in engineering best practices, including code reviews, documentation, and contributing to shared frameworks and tools.

Basic Qualifications
● 4+ years of experience as a Data Engineer or similar role.
● Strong proficiency in SQL (analytical SQL, complex joins, window functions).
● Hands-on experience with PySpark and/or Spark SQL in production.
● Good understanding of data modeling, ETL/ELT design patterns, and distributed data processing.
● Experience building pipelines in Databricks, including Delta Lake, Unity Catalog, data governance, and Lakehouse patterns.
● Experience in AWS (S3, IAM, EC2, Glue, Lambda, or related services).
● Experience with Airflow or similar orchestration tools.
● Experience building robust ingestion pipelines and working with semi‑structured formats (JSON, Parquet, CSV).
● Experience with Git/GitHub, CI/CD, and modern DevOps practices.
● Excellent communication skills and ability to work with cross‑functional partners.