Job Overview

Nextlink is seeking for a Data Engineer with solid expertise in Palantir Foundry, Python, and PySpark to design, develop, and maintain scalable data engineering solutions. The ideal candidate will have hands-on experience with Foundry Ontology, data pipelines, modular applications, and end-to-end data solution implementation, along with a solid understanding of data engineering principles and distributed computing.

Key Responsibilities
1. Palantir Foundry Platform Expertise

- Develop and maintain data pipelines, Foundry Ontology, and modular applications within the Palantir Foundry platform.

- Design and implement end-to-end data solutions, from data ingestion and transformation to data modeling and consumption.

- Integrate data from multiple source systems into Palantir Foundry.

- Build and maintain scalable, reusable data workflows aligned with business requirements.

- Leverage Foundry capabilities to support data-driven applications and analytics.

2. Programming and Scripting – Python & PySpark

- Develop Python and PySpark scripts for data processing, transformation, cleansing, and automation.

- Build reusable Python functions and libraries to extend Palantir Foundry capabilities.

- Develop modular and maintainable code for ETL workflows and data transformation processes.

- Process large datasets efficiently using Python libraries such as Pandas and Num Py, along with PySpark for distributed processing.

- Optimize code for performance, scalability, and reliability.

- Use SQL for data querying, validation, and data modeling, as required.

3. Data Engineering Fundamentals

- Design, develop, and maintain ETL/ELT pipelines for data ingestion and transformation.

- Build efficient data models and optimize data processing workflows.

- Work with structured and unstructured data from multiple sources.

- Implement data quality checks,



validation, and error-handling mechanisms.

- Ensure data consistency, reliability, and availability across pipelines.

4. Big Data and Distributed Systems

- Develop scalable data processing solutions using Apache Spark and PySpark.

- Apply distributed computing concepts to handle large-scale data processing requirements.

- Troubleshoot pipeline failures and resolve data processing issues.

- Perform performance tuning and optimization of Spark jobs and data workflows.

- Follow best practices for scalable and efficient data engineering.

RequirementsEssential Skills

- Hands-on experience with Palantir Foundry and its core capabilities.

- Strong programming skills in Python and PySpark.

- Experience developing and maintaining ETL/ELT pipelines.

- Knowledge of Foundry Ontology, data pipelines, and modular applications.

- Strong understanding of data transformation, data modeling, and data integration.

- Experience with Apache Spark and distributed data processing.

- Understanding of performance optimization and scalable data processing techniques.

Desirable Skills

- Familiarity with SQL for querying and data modeling.

- Experience with Pandas and Num Py for data manipulation and analysis.

- Knowledge of data quality, validation, and pipeline monitoring practices.

- Experience integrating data from multiple source systems.

- Understanding of data governance and enterprise data engineering best practices.

Keywords

Palantir Foundry, Palantir Core, PySpark, Python, Apache Spark, Data Engineering, ETL/ELT, Foundry Ontology, Data Pipelines, Modular Applications, SQL, Data Transformation, Data Modeling, Distributed Computing, Performance Optimization.

Primary Skills: Digital – PySpark, Palantir Core

Role Focus: Palantir Foundry development, Python/PySpark-based data engineering, and end-to-end data pipeline implementation.

📌 Data Engineer (India)
🏢 Next-Link
📍 India