Associate Data Engineer

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.

• 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.