Lead Data Platform Engineer (Palantir or Databricks)

Project description

Role Description - We are seeking an expert with deep proficiency as a Palantir Platform Engineer, possessing experience in data engineering and designing, building, and operationalizing AI-powered workflows, agents, and applications that drive tangible business outcomes.

The ideal candidate is a self-starter, able to translate complex business needs into scalable technical solutions, and confident working directly with stakeholders to maximize the value of Foundry and AIP.

Manage and optimize Palantir data platform.

Ensure high availability, security, and performance of data systems.

Provide valuable insights about data platforms usage.

Optimize computing and storage for large-scale data processing.

Design and maintain system libraries (Python) used in ETL pipelines and platform governance.

Optimize ETL Processes – Enhance and tune existing ETL processes for better performance, scalability, and reliability.

Support the design and deployment of AIP use cases such as copilots, retrieval workflows, and decision-support agents.

Ground agents and logic flows using RAG (retrieval‐augmented generation) by connecting to relevant data sources, embedding/vector search, ontology content.

Use Ontology-Augmented Generation (OAG) when needed: operational decision-making where logic, data, actions and relationships are embedded in the Ontology.

Collaborate with senior engineers on agent design, instructions, and evaluation using AIP's native features.

Must have

Minimum 10 Years of experience in IT/Data.

Minimum 5 years of experience as a Data Platform Engineer/Data Engineer.

Minimum 3 years of experience with Palantir Foundry.

Practical experience using or supporting AIP features such as RAG workflows, copilots, or agent-based applications.

Bachelor's in IT or related field.

Infrastructure & Cloud: Azure, AWS (expertise in storage, networking, compute).

Proficiency in PySpark for distributed computing

Proficiency in Python for ETL development.

SQL: Expertise in writing and optimizing SQL queries, preferably with experience in databases such as PostgreSQL, MySQL, Oracle, or Snowflake.

ETL Tools: Familiarity with ETL tools & processes

Data Modelling: Experience with dimensional modelling, normalization/denormalization, and schema design.

Version Control: Proficiency with version control tools like Git to manage codebases and collaborate on development.

Data Pipeline Monitoring: Familiarity with monitoring tools (e.g., Prometheus, Grafana, or custom monitoring scripts) to track pipeline performance.

Data Quality Tools: Experience implementing data validation, cleaning, and quality frameworks, ideally Monte Carlo.

Nice to have

Containerization & Orchestration: Docker, Kubernetes.

Infrastructure as Code (IaC): Terraform.

Understanding of Investment Data domain (desired).