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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