Palantir Data Engineer

Tekskills Inc.; Mumbai; On-site; INR 2,400,000 - 3,600,000; Full time

Job summary

Tekskills Inc. invites a Palantir Data Engineer to Hyderabad, with 6+ years of experience, to design and optimize data pipelines using Palantir Foundry, ontologies and modular apps.

You will implement end-to-end data solutions, develop Python and PySpark ETL, and collaborate with cross-functional teams to deliver scalable data platforms. Candidates should excel in data modeling and performance tuning for large datasets.

Qualifications

• 6+ years of experience in data engineering or related roles.
• Experience with Palantir Foundry, ontology, pipelines and modular apps.
• Strong programming in Python and PySpark for ETL.
• Experience building end-to-end data solutions and data modeling.

Responsibilities

• Work with Palantir Foundry to build data pipelines and ontology.
• Develop modular applications and end-to-end data solutions.
• Optimize data processing performance using PySpark, Pandas, and NumPy.
• Query and model data using SQL.

Skills

Palantir Platform Expertise

Python

PySpark

Data Processing & Automation

SQL

Tools

Palantir Foundry

Pandas

NumPy

Spark

Job description

Role : Palantir Data Engineer

Experience 6+ yrs

Location - hyderabad

Technical Skills

1. Palantir Platform Expertise

• Working with Foundry Ontology, pipelines, and modular applications.
• Implementing End to End data Solutions in Palantir sourcing data from

2. Programming & Scripting (Python and PySpark)

• Data Processing & Automation: Ability to clean, transform, and process large datasets efficiently.
• Integration with Palantir Foundry: Build custom Python functions and libraries to extend Foundrys capabilities.
• Pipeline Development: Write modular, reusable code for ETL workflows and data transformations.
• Performance Optimization: Use Python libraries like Pandas, NumPy, and PySpark for scalable data operations.
• Familiarity with SQL for querying and data modeling. Good to have

3. Data Engineering Fundamentals

• Building ETL pipelines for ingestion and transformation.
• Designing data models and optimizing workflows.
• Handling structured and unstructured data.

4. Big Data & Distributed Systems

• Experience with Spark (PySpark) for scalable data processing.
• Understanding of parallel computing and performance tuning.