Data Engineer

Promantus Inc
Plano, TX, United States
1 day ago
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Role details

Contract type
Contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source

Tech stack

Application Programming Interfaces (APIs) Airflow Amazon Web Services Amazon S3 Microsoft Azure Big Data Cloud Computing Profiling Databases Information Engineering Data Integration Extract Transform Load (ETL)
+23 more
Data Transformation Data Warehousing Digital Assets Distributed Data Store Apache Hadoop Hadoop Distributed File System Python (Programming Language) PostgreSQL MongoDB MySQL NoSQL NumPy Standard Sql Data Streaming Data Processing Google Cloud Palantir Foundry Database Optimization Apache Spark Pandas Pyspark Data Lineage Data Pipelines

Job description

  • Python Data engineer with DEEP (Palantir experience)

Requirements

  • Strong proficiency in Python and hands-on experience working with Palantir platforms (Foundry, Gotham, or relevant Palantir tools). The ideal candidate will design, build, and optimize data pipelines, support scalable analytics solutions, and ensure reliable data integration within complex environments.

Core Technical Skills

  • Python Programming
  • Advanced knowledge of Python for data processing, ETL development, and automation.
  • Experience with popular libraries: pandas, numpy, pySpark.
  • Ability to write clean, efficient, and modular code following best practices.

Palantir Platforms

  • Practical experience on Palantir Foundry or Gotham (pipelines, data lineage, data modeling).
  • Development, management, and optimization of pipelines and data assets in Palantir environments.
  • Familiarity with Palantir’s ontology, tools for data transformation, and logic building blocks.
  • Integration of external data sources with Palantir, leveraging APIs and connectors.
  • Data Engineering & ETL using Palantir
  • Building, deploying, and managing scalable ETL/ELT pipelines (batch and streaming).
  • Experience with scheduling/orchestrating workflows via Airflow, Palantir Pipeline Builder, or similar.
  • Data quality, validation, profiling, and error handling.
  • Databases and Data Modeling
  • Proficient in SQL; able to work with relational (e.g. PostgreSQL, MySQL) and NoSQL (e.g. MongoDB) databases.
  • Experience designing efficient schemas, indexing strategies, and optimizing queries.
  • Understanding data warehousing concepts.
  • Cloud & Big Data
  • Experience with cloud platforms (AWS, Azure, Google Cloud Platform) and distributed data technologies (e.g. Spark, Hadoop).
  • Familiarity with scalable storage solutions (S3, Blob storage, HDFS).

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