Super urgent position for Data Engineer - Only W2 and Independent Resources - Onsite at New York City, NY

Overview

On Site
Depends on Experience
Full Time

Skills

Spark
Python
Snowflake
Kafka
Hadoop

Job Details

Job Title: Data Engineer

Location: [Remote / Hybrid / Onsite Specify]

Experience: 5-12 Years (Flexible)

Employment Type: Full-Time and W2 resources

Job Summary

We are seeking a highly skilled Data Engineer to design, build, and maintain scalable data pipelines and platforms. The ideal candidate will have strong hands-on experience with Snowflake, Python, Apache Spark, Kafka, and Hadoop, and a passion for transforming large volumes of data into reliable, high-performance data solutions that support analytics, reporting, and machine learning use cases.

Key Responsibilities

  • Design, develop, and maintain end-to-end data pipelines using Python, Spark, and Hadoop.

  • Build and optimize Snowflake data warehouses, including schema design, data modeling, and performance tuning.

  • Develop real-time and batch data ingestion pipelines using Apache Kafka.

  • Process and transform large datasets using Apache Spark (PySpark).

  • Integrate data from multiple structured and unstructured sources.

  • Implement data quality checks, validation, and monitoring frameworks.

  • Optimize query performance, storage, and cost efficiency in Snowflake.

  • Collaborate with Data Scientists, Analysts, and Product teams to deliver high-quality data solutions.

  • Ensure data security, governance, and compliance best practices.

  • Troubleshoot and resolve data pipeline and performance issues.

Required Skills & Qualifications

  • Strong programming experience in Python.

  • Hands-on expertise with Snowflake (data modeling, SQL, performance optimization).

  • Experience with Apache Spark (PySpark preferred).

  • Solid knowledge of Apache Kafka for real-time streaming data pipelines.

  • Working experience with Hadoop ecosystem (HDFS, Hive, YARN, etc.).

  • Strong SQL skills for data transformation and analytics.

  • Experience building scalable ETL/ELT pipelines.

  • Understanding of data warehousing concepts and dimensional modeling.

  • Familiarity with Linux/Unix environments.

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