DataOps Engineer

DataOps Engineer (Data Curation at large scale for LLM training)

This role is of an experienced DataOps Engineer to lead the design, implementation, and optimization of our large-scale data program. As a DataOps Engineer, you will be responsible for developing and maintaining robust data pipelines, automating ETL processes, ensuring data quality and consistency, and implementing monitoring and alerting mechanisms. Leveraging Microsoft Azure services, tools, platforms, and products, you will drive efficiency and scalability across our data infrastructure while adhering to best practices and industry standards. This is a program that curates spatial, temporal and other varieties of data from SQL databases for LLM training to build analytics GPT application

Key Responsibilities:

1. Design, build, and maintain scalable ETL pipelines to extract, transform, and load data from diverse sources into our data lake or warehouse, utilizing Microsoft Azure Data Factory, Azure Databricks, and other relevant technologies.
2. Develop automation scripts and workflows to streamline data ingestion, cleaning, and curation processes, ensuring timely and accurate delivery of data to downstream applications and analytics platforms.
3. Implement data cleaning, enrichment, and normalization techniques to ensure data quality and consistency, collaborating with data analysts and domain experts to define data validation rules and standards.
4. Leverage Microsoft Azure services such as Azure Machine Learning, Azure Data Lake Storage, and Azure SQL Database to perform data vectorization, feature engineering, and advanced analytics tasks, driving actionable insights and predictive modeling capabilities.
5. Establish robust monitoring and alerting systems to proactively identify and address data quality issues, performance bottlenecks, and security threats, utilizing Azure Monitor, Azure Log Analytics, and other monitoring tools.
6. Work closely with cross-functional teams including data scientists, software engineers, and business stakeholders to understand data requirements, define data models, and deliver data solutions that meet business objectives.
7. Continuously evaluate and optimize the performance, reliability, and cost-effectiveness of our data infrastructure, identifying opportunities for automation, optimization, and cloud-native innovations.
8. Stay abreast of the latest developments in data management, cloud computing, and DevOps practices, and contribute to the company's knowledge base through research, training, and knowledge sharing activities.

Qualifications:

• 8-10 years of experience in data engineering, dataops,DevOps, or related roles, with a focus on designing and implementing data pipelines and data processing workflows.
• Knowledge of Azure OpenAI GPT models and Azure AI Search indexes,indexers and vector stores
• Strong knowledge in text to sql capability of LLMs using SQL agents
• Proficiency in Microsoft Azure services, platforms, and products, with hands-on experience in Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Blob Storage, etc.
• Strong programming skills in languages such as Python, SQL, or Scala, and experience with version control systems (e.g., Git) and CI/CD pipelines.
• Solid understanding of data management concepts, ETL processes, data quality principles, and data governance frameworks.
• Excellent problem-solving skills, with the ability to troubleshoot complex data issues and optimize performance in a cloud environment.
• Strong communication and collaboration skills, with the ability to work effectively in a cross-functional team and communicate technical concepts to non-technical stakeholders.