Module Lead - Palantir

We are looking forward to hirePalantir Professionals in the following areas :

SCOPE OF RESPONSIBILITY:
As a Data Architect Data Engineering, you will play a key role in shaping Client Life Science's modern data ecosystem that integrates Palantir Foundry, AWS cloud services, and Snowflake. Your responsibility will be to architect scalable, secure, and high-performing data pipelines and platforms that power advanced analytics, AI/ML use cases, and digital solutions across the enterprise.You will lead design efforts and provide architectural governance across data ingestion, transformation, storage, and consumption layersensuring seamless interoperability across platforms while enabling compliance, performance, and cost-efficiency. PURPOSE OF THE POSITION:The role aims to define and implement future-ready, cloud-native, and platform-agnostic data architecture that unifies business, scientific, and operational data assets. You will enable Clients Life Science organization to make informed, data-driven decisions at scale, while ensuring architectural alignment with security, governance, and business agility.

ROLES & RESPONSIBILITIES:

• Design and maintain an integrated data architecture that connects Palantir Foundry, AWS services, and Snowflake, ensuring secure, scalable, and performant data access.
• Define and govern enterprise-wide standards for data modeling, metadata, lineage, and security across hybrid environments.
• Architect high-throughput data pipelines that support batch and real-time ingestion from APIs, structured/unstructured sources, and external platforms.
• Collaborate with engineering, analytics, and product teams to implement analytical-ready data layers across Foundry, Snowflake, and AWS-based lake houses.
• Define data optimization strategies including partitioning, clustering, caching, and materialized views to improve query performance and reduce cost.
• Ensure seamless data interoperability across Palantir objects (Quiver, Workshop, Ontology), Snowflake schemas, and AWS S3-based data lakes.
• Lead DataOps adoption including CI/CD for pipelines, automated testing, quality checks, and monitoring.
• Govern identity and access management using platform-specific tools (e.g., Foundry permissioning, Snowflake RBAC, AWS IAM).
• Drive compliance with data governance frameworks, including auditability, PII protection, and regulatory requirements (e.g., GxP, HIPAA).
• Evaluate emerging technologies (e.g., vector databases, LLM integration, Data Mesh) and provide recommendations.
• Act as an architectural SME during Agile Program Increment (PI) and Sprint Planning sessions.

EDUCATION & CERTIFICATIONS:

• Bachelors or Masters degree in Computer Science, Data Engineering, or a related field.
• AWS/ Palantir/ Snowflake Architect or Data Engineer Certification (preferred).

EXPERIENCE:

• 6-8 Years of data engineering and architecture experience, with at least:
• Hands-on experience with Palantir Foundry (Quiver pipelines, Workshop, Ontology design).
• Working knowledge on AWS data services (S3, Glue, Redshift, Lambda, IAM, Athena).
• Working with Snowflake (warehouse design, performance tuning, secure data sharing).
• Domain experience in life Science/Pharma or regulated environments preferred.

TECHNICAL SKILLS:

• Data Architecture: Experience with hybrid data lake/data warehouse architecture, semantic modeling, and consumption layer design.
• Palantir Foundry: Proficiency in Quiver pipelines, Workshop applications, Ontology modeling, and Foundry permissioning.
• Snowflake: Deep understanding of virtual warehouses, time travel, data sharing, access controls, and cost optimization.
• AWS: Strong experience with S3, Glue, Redshift, Lambda, Step Functions, Athena, IAM, and monitoring tools (e.g., CloudWatch).
• ETL/ELT: Strong background in batch/streaming data pipeline development using tools such as Airflow, dbt, or NiFi.
• Programming: Python, SQL (advanced), Shell scripting; experience with REST APIs and JSON/XML formats.
• Data Modeling: Dimensional modeling, third-normal form, NoSQL/document structures, and modern semantic modeling.
• Security & Governance: Working knowledge of data encryption, RBAC/ABAC, metadata catalogs, and data classification.
• DevOps & DataOps: Experience with Git, Jenkins, Terraform/CloudFormation, CI/CD for data workflows, and observability tools.

SOFT SKILLS:

• Strategic and analytical thinking with a bias for action.
• Strong communication skills to articulate architecture to both technical and business audiences.
• Ability to work independently and collaboratively in cross-functional, global teams.
• Strong leadership and mentoring capability for junior engineers and architects.
• Skilled in stakeholder management, technical storytelling, and influencing without authority.

GOOD-TO-HAVE SKILLS:

• Experience with Data Mesh or federated governance models.
• Integration of AI/ML models or LLMs with enterprise data architecture.
• Familiarity with business intelligence platforms (e.g., Tableau, Power BI) for enabling self-service analytics.
• Exposure to vector databases or embedding-based search systems.