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. 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.
Minimum 10 Years of experience in IT/Data.
Minimum 5 years of experience as a Data Platform Engineer/Data Engineer.
Bachelor's in IT or related field.
Infrastructure & Cloud: Azure, AWS (expertise in storage, networking, compute).
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: 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.
Docker, Kubernetes.
Infrastructure as Code (IaC): Terraform. xbhjioe
Understanding of Investment Data domain (desired).