Palantir Data Engineer

Tekskills; Hyderabad; On-site; INR 900,000 - 1,600,000; Full time

Job summary

Tekskills in Hyderabad seeks a Palantir Data Engineer with 6+ years of experience to design and implement data solutions using Palantir Foundry, focusing on pipelines, ontology, and modular applications to source and transform data.

Proficiency in Python, PySpark and SQL is essential, with hands-on experience building scalable ETL workflows, data models, and performance tuning in distributed environments.

Qualifications

• 6+ years of data engineering experience.
• Proficient with Palantir Foundry including ontology, pipelines, and modular apps.
• Experience building end-to-end data solutions and ETL workflows.

Responsibilities

• Build and optimize ETL pipelines for ingestion and transformation.
• Design data models and optimize data workflows in distributed environments.
• Develop and extend Palantir Foundry capabilities with Python libraries.
• Collaborate with data teams to ensure scalable data processing and quality.

Skills

Palantir Foundry

Python

PySpark

SQL

ETL pipelines

Data modeling

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.