Oncorre Inc
Lead Data Engineer – Palantir & PySpark (Remote) – Experience in Reinsurance Domain
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Lead Data Engineer with 6–10 years of experience, focusing on Palantir and PySpark in the reinsurance domain. Remote work offered, requiring expertise in SQL, cloud platforms, and data governance.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
November 5, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
New Jersey, United States
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🧠 - Skills detailed
#Data Governance #Data Integration #Data Ingestion #Automation #Databricks #Scala #Snowflake #Spark SQL #SQL (Structured Query Language) #Agile #Datasets #Data Architecture #REST (Representational State Transfer) #GCP (Google Cloud Platform) #Data Management #"ETL (Extract #Transform #Load)" #Data Lineage #PySpark #Python #Business Analysis #Version Control #GIT #Spark (Apache Spark) #Data Pipeline #Security #Data Security #Palantir Foundry #S3 (Amazon Simple Storage Service) #AWS (Amazon Web Services) #REST services #Compliance #Metadata #Azure #Data Engineering #Cloud #Data Modeling
Role description
Lead Data Engineer – Palantir & PySpark
Experience: 6–10 Years
Location : Remote
Client Industry: Reinsurance
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Job Summary:
We are seeking a highly skilled Data Engineer with hands-on experience in Palantir (Foundry preferred), PySpark, and exposure to reinsurance or insurance data environments. The ideal candidate will play a key role in building scalable data pipelines, optimizing ETL workflows, and enabling advanced analytics and reporting capabilities. This role requires a strong technical foundation in data engineering combined with an understanding of the reinsurance business domain.
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Key Responsibilities:
· Design, develop, and maintain data pipelines and ETL workflows using PySpark, SQL, and Palantir Foundry.
· Collaborate with data architects, business analysts, and actuarial teams to understand reinsurance data models and transform complex datasets into usable formats.
· Build and optimize data ingestion, transformation, and validation processes to support analytical and reporting use cases.
· Work within the Palantir Foundry platform to design robust workflows, manage datasets, and ensure efficient data lineage and governance.
· Ensure data security, compliance, and governance in line with industry and client standards.
· Identify opportunities for automation and process improvement across data systems and integrations.
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Required Skills & Qualifications:
· 6–10 years of overall experience in data engineering roles.
· Strong hands-on expertise in PySpark (dataframes, RDDs, performance optimization).
· Proven experience working with Palantir Foundry or similar data integration platforms.
· Good understanding of reinsurance including exposure, claims, and policy data structures.
· Proficiency in SQL, Python, and working with large datasets in distributed environments.
· Experience with cloud platforms (AWS, Azure, or GCP) and related data services (e.g., S3, Snowflake, Databricks).
· Knowledge of data modeling, metadata management, and data governance frameworks.
· Familiarity with CI/CD pipelines, version control (Git), and Agile delivery methodologies.
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Preferred Skills:
· Experience with data warehousing and reporting modernization projects in the reinsurance domain.
· Exposure to Palantir ontology design and data operationalization.
· Working knowledge of APIs, REST services, and event-driven architecture.
· Understanding of actuarial data flows, submission processes, and underwriting analytics is a plus.
Lead Data Engineer – Palantir & PySpark
Experience: 6–10 Years
Location : Remote
Client Industry: Reinsurance
---
Job Summary:
We are seeking a highly skilled Data Engineer with hands-on experience in Palantir (Foundry preferred), PySpark, and exposure to reinsurance or insurance data environments. The ideal candidate will play a key role in building scalable data pipelines, optimizing ETL workflows, and enabling advanced analytics and reporting capabilities. This role requires a strong technical foundation in data engineering combined with an understanding of the reinsurance business domain.
---
Key Responsibilities:
· Design, develop, and maintain data pipelines and ETL workflows using PySpark, SQL, and Palantir Foundry.
· Collaborate with data architects, business analysts, and actuarial teams to understand reinsurance data models and transform complex datasets into usable formats.
· Build and optimize data ingestion, transformation, and validation processes to support analytical and reporting use cases.
· Work within the Palantir Foundry platform to design robust workflows, manage datasets, and ensure efficient data lineage and governance.
· Ensure data security, compliance, and governance in line with industry and client standards.
· Identify opportunities for automation and process improvement across data systems and integrations.
---
Required Skills & Qualifications:
· 6–10 years of overall experience in data engineering roles.
· Strong hands-on expertise in PySpark (dataframes, RDDs, performance optimization).
· Proven experience working with Palantir Foundry or similar data integration platforms.
· Good understanding of reinsurance including exposure, claims, and policy data structures.
· Proficiency in SQL, Python, and working with large datasets in distributed environments.
· Experience with cloud platforms (AWS, Azure, or GCP) and related data services (e.g., S3, Snowflake, Databricks).
· Knowledge of data modeling, metadata management, and data governance frameworks.
· Familiarity with CI/CD pipelines, version control (Git), and Agile delivery methodologies.
---
Preferred Skills:
· Experience with data warehousing and reporting modernization projects in the reinsurance domain.
· Exposure to Palantir ontology design and data operationalization.
· Working knowledge of APIs, REST services, and event-driven architecture.
· Understanding of actuarial data flows, submission processes, and underwriting analytics is a plus.
