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Data Engineer | Databricks + Snowflake + Azure +Palantir Foundry:: W2 Only(H1 Transfer)

Bright SolPlano, TX🇺🇸United StatesPosted 9 Sept 2026

Why This Role Stands Out

This role offers a fantastic opportunity to leverage your expertise in Databricks, Snowflake, Azure, and Palantir Foundry to drive impactful data solutions. You'll thrive here if you're a skilled Data Engineer eager to expand your capabilities within a reputable company. Apply today to take your career to the next level!

Quick Overview

Seniority
Mid Senior
Work mode
On Site
Location
Plano, TX, United States
Posted
Yesterday
SQLETLSnowflakeAirflowApacheAzureDatabricksGitKafkaPythonUnitydbt

Job Description

Position Details

  • Role: Data Engineer
  • Locations: Plano, TX | Alpharetta, GA | Middletown, NJ
  • Duration: 12+ months, with potential extensions
  • Employment Type: W2 Only
  • Work Arrangement: Onsite – 5 days/week initially

Required Skills

  • 3+ years of hands-on Data Engineering experience
  • Strong Python, PySpark, and SQL
  • Hands-on Databricks experience
  • Strong Snowflake experience
  • Azure experience with ADF, ADLS Gen2, Synapse, and Databricks
  • Hands-on Palantir Foundry experience
  • ETL/ELT pipeline development
  • Data modeling – Star Schema / Snowflake Schema
  • Airflow, dbt, or similar orchestration tools
  • Data quality, governance, metadata, and security
  • Git and CI/CD
  • Batch and/or streaming data pipelines

Key Responsibilities

  • Design and develop scalable data pipelines using Palantir Foundry, Databricks, and Snowflake
  • Build ETL/ELT workflows using Python, PySpark, and SQL
  • Develop Azure-based data solutions using ADF, ADLS, Synapse, and Databricks
  • Support enterprise data migration and modernization initiatives
  • Implement data quality, governance, metadata, and lineage
  • Optimize Spark/Databricks workloads and Snowflake queries
  • Develop dimensional data models and analytical datasets
  • Support batch and near-real-time data pipelines
  • Troubleshoot production pipeline, data quality, and performance issues
  • Implement CI/CD and collaborate with data scientists, analysts, and business stakeholders

Preferred / Nice-to-Have

  • Delta Lake / Apache Iceberg
  • Unity Catalog
  • Kafka / Spark Streaming / Azure Event Hubs
  • Snowflake clustering and materialized views
  • IBM WatsonX.data or similar AI data platforms
  • GenAI / RAG / LangChain
  • MCP

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