Roles and Responsibilities :

- Design, develop, test, and deploy scalable data pipelines using Snowflake as the primary database engine.
- Collaborate with cross-functional teams to gather requirements and design data models that meet business needs.
- Develop high-quality code in Python using Palantir Foundry's Data Engineering framework to ensure efficient processing of large datasets.
- Troubleshoot issues related to data quality, performance, and security in AWS IAM environments.

Job Requirements :

- 6-8 years of experience in Data Engineering with expertise in Snowflake as a primary database engine.
- Strong understanding of data architecture principles and ability to design scalable data models.
- Proficiency in developing complex SQL queries for querying large datasets using Snowflake's native language (SQL).
- Experience working with cloud-based platforms such as AWS IAM for identity management.

***EDUCATION & CERTIFICATIONS:
- Bachelor’s or Master’s 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.
- Solid 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.***

📌 Associate Data Engineer (Bengaluru)
🏢 Quantiphi
📍 Bengaluru