HYR Global Source Inc

Databricks Data Engineer @ Spring, TX (Onsite - NEED LOCALS) - W2 Only, ANY VISA

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Databricks Data Engineer in Spring, TX, lasting 12 months. Required skills include 6 years of data engineering experience, Databricks, Spark, Python, SQL, and ETL pipeline development. A Bachelor's degree in a related field is necessary.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
June 24, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
On-site
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
Spring, TX
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🧠 - Skills detailed
#"ETL (Extract #Transform #Load)" #Data Quality #Data Ingestion #SQL (Structured Query Language) #Scala #Databricks #Delta Lake #Spark (Apache Spark) #PySpark #Python #Computer Science #Data Engineering #Spark SQL
Role description
Role: Databricks Data Engineer Location: Spring, TX - 77389 (Onsite) - NEED LOCALS Duration: 12 Months + Extension W2 only - ANY VISA... Work Authorization: U.S. Citizens and Green Card Holders preferred, all valid work authorizations may apply Education Requirement - Bachelor s Degree in: Computer Science, Information Technology, Or related field Education Requirement - Bachelor s Degree in: Computer Science, Information Technology, Or related field Required Skills • 6 years experience in data engineering • Hands-on experience with Databricks and Spark • Strong Python and SQL skills • Experience building ETL pipelines • Understanding of Delta Lake and distributed systems Role Overview The Databricks Data Engineer is responsible for building and maintaining data ingestion and transformation pipelines within Databricks. This role focuses on bringing data from multiple source systems into the Lakehouse and preparing it for downstream analytics. Key Responsibilities • Develop and maintain data ingestion pipelines from multiple sources • Build ETL/ELT pipelines using Databricks (PySpark, SQL) • Implement medallion architecture (Bronze, Silver, Gold) • Ensure data quality and validation across pipelines • Optimize pipeline performance and scalability • Work with structured and semi-structured data formats • Monitor pipeline execution and resolve issues