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Job Description
Structured overview of role & requirementsAbout This Role
Design and build ingestion pipelines and reusable connectors for structured and unstructured data from enterprise, third-party, and licensed sources.
Develop and maintain data layers from raw data to marts serving analytics, AI use cases, and LLM agents including metadata, semantic, and vector indexes.
Implement data quality frameworks, lineage tracking, security controls, and support SIT/UAT validation and production releases.
Minimum Requirements
3 to 6 years data engineering experience; Senior Data Engineer requires 2+ years owning data foundation architecture and connector frameworks.
Proficiency in Python, SQL, and data modeling (dimensional and normalized).
Experience with modern data platforms such as Databricks, Snowflake, or Azure/AWS data services and pipeline orchestration tools like Spark, Airflow, ADF, or dbt.
Strong skills in API integration (REST/JSON), incremental ingestion (CDC), data quality frameworks, version control (Git), and CI/CD for data workloads.
Ideal Candidate Profile
Experienced in designing scalable data architectures that support AI, analytics, and large language model (LLM) agent applications with grounded and traceable data.
Practiced in handling unstructured content, metadata management, vector databases, and knowledge graphs, ideally in regulated or life sciences domains.
Familiar with cloud-native data services and holds relevant certifications (Azure Data Engineer, AWS, Databricks, or Snowflake).