Description

Key Responsibilities

Data Engineering

• Design, develop, and optimize scalable data pipelines using Snowflake, dbt, SQL, and Python.
• Build and maintain enterprise-scale ETL/ELT pipelines and data transformation workflows.
• Develop reusable dbt models, macros, tests, and documentation.
• Design and maintain scalable data models for analytics and AI/ML applications.
• Integrate structured and unstructured data from multiple enterprise sources.
• Develop data solutions that support downstream analytics, reporting, and machine learning workloads.

Snowflake & Data Platform

• Design, develop, and optimize Snowflake data warehouse solutions.
• Perform query optimization, workload tuning, and performance troubleshooting.
• Optimize data platforms for scalability, reliability, and cost efficiency.
• Implement appropriate data partitioning, clustering, and transformation strategies.
• Monitor data platform performance and resolve production issues.

AI/ML & GenAI

• Prepare high-quality datasets and feature data to support AI/ML model development and training.
• Support Generative AI and RAG use cases through structured and unstructured data integration.
• Develop and maintain data pipelines supporting AI/ML applications.
• Work with vector databases and data retrieval solutions for AI applications.
• Develop APIs and data services that support AI/ML applications and automation.
• Collaborate with data scientists and AI engineers to operationalize data solutions for production AI workloads.

Data Quality & Governance

• Implement data quality, validation, monitoring, and reconciliation processes.
• Support data governance, security, access controls, and data lineage initiatives.
• Ensure data pipelines meet enterprise security and compliance requirements.
• Establish monitoring and alerting mechanisms for critical data workflows.

Collaboration & Production Support

• Collaborate with business stakeholders, data analysts, data scientists, and engineering teams to understand and implement data requirements.
• Troubleshoot complex production data issues and provide timely resolutions.
• Participate in code reviews and follow engineering best practices.
• Support CI/CD and automated deployment processes for data engineering workloads.
• Contribute to Agile development, planning, and delivery activities.

Required Skills

• 8+ years of overall IT experience, with 6–8 years of strong core Data Engineering experience.
• Strong hands-on experience with Snowflake.
• Strong experience with dbt.
• Advanced SQL skills.
• Strong Python programming experience.
• Experience building enterprise-scale ETL/ELT pipelines.
• Strong knowledge of data modeling and data warehouse architecture.
• Experience with AI/ML data engineering and production data solutions.
• Hands-on exposure to Generative AI and RAG architectures.
• Experience with vector databases and AI/ML data pipelines.
• Experience with at least one major cloud platform: AWS, Azure, or GCP.
• Experience with Git and CI/CD.
• Understanding of data governance, security, access controls, data quality, and lineage.
• Strong troubleshooting and performance optimization skills.

Preferred / Nice-to-Have Skills

• UNIX / Linux
• Control-M
• DataStage
• FastAPI
• Kafka
• Apache Airflow
• Git / CI/CD
• Agile / Scrum methodologies
• REST APIs and microservices
• Cloud-native data engineering

Key Skills

Education

Bachelor's degree