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# Full Stack Python Developer - NJ

- **Company:** PHOTON, LLC
- **Location:** United States
- **Experience:** Expert
- **Salary:** $38,000.0 - $133,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Apache HTTP Server, Microsoft Azure, Cloud Computing, Databases, Extract Transform Load (ETL), Data Masking, Distributed Computing Environment, Python (Programming Language), Performance Tuning, Standard Sql, SQL Databases, Pulumi, Data Build Tool (dbt), Apache Spark, Infrastructure as Code (IaC), Data Layers, Microsoft Fabric, Data Lakes, Pyspark, Kubernetes, Star Schema, Presto, Data Lakehouse, Terraform, Data Pipelines, Databricks
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e8b79dc3572aa8d5

## About the Role

* Python & Spark: 5+ years of experience with Python and deep expertise in Apache Spark tuning (partitioning, shuffling, caching).
 * Data Federation Tools: Hands-on experience with Starburst Enterprise , Trino (Presto) , or Dremio .
 * Lakehouse Ecosystem: Proven track record working with Delta Lake or Iceberg architectures.
 * Cloud Platforms: Extensive experience with AWS (EMR, S3, Glue) , Azure (Databricks, ADLS) , or GCP .
 * SQL Mastery: Expert-level SQL skills for complex analytical queries and query plan analysis.
 * Data Modeling: Proficiency in designing Star/Snowflake schemas and understanding "Medallion Architecture" (Bronze, Silver, Gold layers).

### Preferred "Bonus" Skills

 * Experience with Infrastructure as Code (IaC) like Terraform or Pulumi.
 * Familiarity with dbt (data build tool) for modeling within the federation layer.
 * Knowledge of Kubernetes (K8s) for deploying and scaling Spark/Trino clusters.
 * Background in Data Mesh or Data Fabric methodologies.

## Description

As a Senior Data Engineer , you will be responsible for breaking down data silos. This role focuses on building a unified, high-performance data layer using Data Federation techniques. You won't just move data; you will architect a Data Lakehouse environment where disparate sources feel like a single, cohesive database for our analytics and AI teams., * Data Federation Architecture: Design and implement federated query layers (e.g., Starburst/Trino ) to allow high-speed analytics across distributed data sources without unnecessary data movement.

 * ETL/ELT Pipeline Development: Build scalable, distributed data processing pipelines using Python and Apache Spark (PySpark).
 * Lakehouse Implementation: Manage and optimize modern table formats like Delta Lake , Apache Iceberg , or Hudi to bring ACID transactions to our data lake.
 * Performance Tuning: Optimize Spark jobs and SQL queries across the federation layer to minimize latency and manage compute costs.
 * Governance & Security: Implement fine-grained access control and data masking within the federation engine to ensure data privacy across all connected platforms.


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