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# Data Engineer | Onsite | Sunnyvale, California

- **Company:** Photon Interactive Services Inc.
- **Location:** Sunnyvale, CA, United States
- **Experience:** Expert
- **Salary:** $48,000 - $168,000
- **Contract:** Permanent contract
- **Skills:** Query Performance, Airflow, Amazon Web Services, Bash Shell, Data Validation, Information Engineering, Data Files, Data Governance, Extract Transform Load (ETL), Data Warehousing, Dimensional Modeling, Python (Programming Language), Performance Tuning, SQL Databases, Workflow Management Systems, Scripting, Sql Optimization, Snowflake, Star Schema, Data Management, Data Pipelines
- **Published:** October 30, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-engineer-onsite-sunnyvale-california-sunnyvale-ca--129e9183-6dc9-4af6-bb37-1b788a17e641

## About the Role

Data Sets, Snowflake Schema, Data Management, Data Warehousing, Python Programming/Scripting Language, SQL (Structured Query Language), Data Modeling, Identify Issues, Scripting (Scripting Languages), Data Quality, * 7+ years of experience in data engineering or a closely related field.

 * Strong Python skills and experience with scripting languages (for example Bash).
 * Advanced SQL, including complex joins, window functions, and performance tuning.
 * Hands-on experience with Snowflake (warehouses, stages, tasks, streams, role-based access).
 * Solid understanding of data warehousing, dimensional modeling, and ETL/ELT patterns., * Experience with orchestration tools (for example Airflow) and transformation frameworks (for example dbt).
 * Familiarity with AWS or GCP data services.
 * Exposure to data governance, security, and privacy practices.

Compensation, Benefits and Duration

## Description

* Design, build, and operate batch and incremental data pipelines using Python and SQL.
 * Develop and optimize data models and transformations in Snowflake.
 * Write automation and operational scripts to orchestrate, monitor, and troubleshoot data workflows.
 * Implement data quality checks, validation, lineage, and alerting.
 * Tune query performance and manage warehouse cost and resource usage.
 * Collaborate with analysts, data scientists, and application teams to understand requirements and deliver datasets.
 * Document pipelines, schemas, and data contracts.