Data Engineer

Data Engineer

Mandatory Skills: Gen AI, Data Engineering, ETL Jobs, Snowflake, Azure Cloud

Role & Responsibilities

Data Engineer – Essential Job Functions:

• Design, develop, and maintain scalable data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
• Implement efficient data processing workflows to support the training and evaluation of solutions using large language models, ensuring reliability, scalability, and performance.
• Addressing issues related to data quality, pipeline failures, or resource contention, ensuring minimal disruption to systems.
• Integrate Large Language Model into data pipeline for natural language processing tasks.
• Working with Snowflake ecosystem
• Deploying, scaling, and monitoring AI solutions on cloud platforms like Snowflake, Azure, AWS, GCP
• Communicating technical and non-technical stakeholders and collaborate with cross-functional teams.
• Cloud cost management and best practices to optimize cloud resource usage and minimize costs.

Data Engineer – Preferred Qualifications:

• Experience working within the Azure ecosystem, including Azure AI Search, Azure Storage Blob, Azure Postgres and understanding how to leverage them for data processing, storage, and analytics tasks.
• Experience with techniques such as data normalization, feature engineering, and data augmentation.
• Ability to preprocess and clean large datasets efficiently using Azure Tools /Python and other data manipulation tools.
• Expertise in working with healthcare data standards (ex. HIPAA and FHIR), sensitive data and data masking techniques to mask personally identifiable information (PII) and protected health information (PHI) is essential.
• In-depth knowledge of search algorithms, indexing techniques, and retrieval models for effective information retrieval tasks.
• Familiarity with search platforms like Elasticsearch or Azure AI Search is a must.
• Familiarity with chunking techniques and working with vectors and vector databases like Pinecone.
• Experience working within the snowflake ecosystem.
• Ability to design, develop, and maintain scalable data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
• Experience with implementing best practices for data storage, retrieval, and access control to ensure data integrity, security, and compliance with regulatory requirements.
• Be able to implement efficient data processing workflows to support the training and evaluation of solutions using large language models, ensuring reliability, scalability, and performance.
• Ability to proactively identify and address issues related to data quality, pipeline failures, or resource contention, ensuring minimal disruption to systems.
• Experience with large language model frameworks, such as Langchain and know how to integrate them into data pipelines for natural language processing tasks.