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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, Retrieval-Augmented Generation, Apache Spark, Data Layers, Star Schema, Apache Kafka, Software Version Control, Data Pipelines
- **Published:** October 10, 2026
- **Apply:** https://www.dice.com/job-detail/b964e300-5e62-4a05-8ede-6f49da479572

## 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.