Data Engineer (Local & W2 Candidates)

Title: Data Engineer

Location: Plano, TX
Work Model: 5 Days Onsite
Experience: 12+ Years
Employment Type: W2 Contract
Rate: $80/hr on W2
Local/Nearby Candidates: Required

Position Overview

We are looking for an experienced Data Engineer with 12+ years of overall IT/data engineering experience and strong hands-on expertise in Databricks, Snowflake, Apache Spark, Java, and Python.

The ideal candidate will have a strong background in designing and developing scalable data pipelines, data processing solutions, ETL/ELT workflows, and modern cloud data platforms. Experience with AWS and Amazon EMR is highly preferred.

Required Skills

• 12+ years of overall IT/Data Engineering experience
• Strong hands-on experience with Databricks
• Strong experience with Snowflake
• Extensive experience with Apache Spark / PySpark
• Strong programming experience with Java and/or Python
• Strong SQL and data engineering fundamentals
• Experience designing and developing scalable ETL/ELT data pipelines
• Experience working with large-scale data processing and distributed computing
• Strong understanding of data warehouse/data lake architectures
• Ability to troubleshoot and optimize data pipelines and Spark jobs

Preferred / Nice-to-Have Skills

• AWS
• Amazon EMR
• AWS S3
• AWS Glue
• AWS data services
• Delta Lake / Lakehouse architecture
• Spark SQL
• Data pipeline optimization and performance tuning

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Databricks and Apache Spark.
• Develop complex data transformation and processing workflows using Python, Java, PySpark, and SQL.
• Build and optimize data solutions using Snowflake.
• Develop ETL/ELT pipelines for large-scale structured and unstructured datasets.
• Perform data processing and transformation using distributed computing technologies.
• Optimize Spark jobs, Databricks workloads, SQL queries, and Snowflake processes.
• Integrate data from multiple enterprise data sources into modern data platforms.
• Work with AWS services and Amazon EMR where applicable.
• Troubleshoot data pipeline failures and production issues.
• Collaborate with architects, developers, analysts, and other engineering teams to deliver reliable data solutions.
• Follow best practices around data quality, scalability, performance, and maintainability.