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# Data Platform Engineer

- **Company:** Codeforce 360
- **Location:** Cupertino, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Data Analysis, Apache HTTP Server, Continuous Integration, Data Infrastructure, Data Warehousing, Machine Learning, Operational Databases, Recommender Systems, Scala (Programming Language), SQL Databases, Feature Engineering, Apache Spark, Data Layers, Star Schema, Apache Kafka, Software Version Control, Data Pipelines
- **Published:** August 28, 2026
- **Apply:** https://www.dice.com/job-detail/fbc061bd-243b-40d1-8e61-b6d813a06002

## About the Role

About the Job- We are looking for an experienced Data Platform Engineer to join one of our enterprise client engagements. The ideal candidate should have strong expertise in SQL, Spark, Kafka, AI/ML along with experience working in fast-paced enterprise environments., * Experience designing, developing, and deploying analytical data products.

 * Senior IC leading data products and visualizations across multiple lines of business.
 * Strong hands-on experience with SQL, Scala, and Apache Spark.
 * Experience with data pipeline technologies such as Kafka and Airflow.
 * Strong knowledge of data warehousing, dimensional/star schema modeling, and metric standardization.
 * Experience with CI/CD and version control practices.
 * Experience building and maintaining large-scale production data pipelines.
 * Excellent communication skills with both technical and non-technical/executive stakeholders., * Production experience with SQL, Spark, and Scala.
 * Experience with modern data platforms/Lakehouse technologies such as Apache Iceberg, Trino, and Superset.
 * Strong understanding of analytics, data modeling, and metric design.
 * Experience with AI/ML, recommendation systems, feature engineering, GenAI, RAG, or AI-powered data products is a plus.

## Description

* Own the strategy, design, and end-to-end delivery of analytical data products and visualizations.
 * Translate business requirements into instrumentation, metrics, semantic layers, and data models.
 * Standardize metrics and semantic layers across multiple lines of business.
 * Partner with executive and cross-functional stakeholders across technical and business teams.
 * Work closely with engineering teams on data pipelines, data modeling, and delivery workflows.
 * Ensure scalability, performance, reliability, and data quality of analytical data products.
 * Investigate complex data issues and develop actionable insights.


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