Summary:
We are looking for a highly motivated and experienced Data Engineer to join our data engineering team. The ideal candidate will have a strong background in building scalable data pipelines using the AWS cloud stack and extensive hands-on experience with Snowflake. Proficiency in Python and SQL, along with graph and vector database technologies, is essential. This role requires strong problem-solving abilities and a proactive mindset to deliver efficient, scalable, and reliable data solutions.
Responsibilities:
- Design, develop, and maintain scalable data pipelines on AWS using services such as S3, Glue, Lambda, Redshift, and EMR.
- Build and optimize data warehousing solutions using Snowflake, including performance tuning and data modeling.
- Write efficient and reusable code in Python and SQL for data transformation and processing.
- Collaborate with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand data requirements.
- Integrate vector databases with LLM-based applications and AI workflows.
- Monitor, troubleshoot, and improve pipeline performance and reliability.
- Ensure data quality, integrity, and security across all stages of the pipeline.
- Participate in code reviews, architecture discussions, and continuous improvement initiatives.
Requirements:
- 7-10 years of experience in data engineering or related roles.
- Bachelor&rsquos or Master&rsquos degree in Computer Science, Engineering, or a related field.
Required Skills:
- Strong hands-on experience with AWS cloud services, including data and AI workloads.
- Deep understanding of Snowflake architecture, performance tuning, and best practices.
- Advanced proficiency in Python and SQL for data pipelines, transformations, and services.
- Hands-on experience with graph databases (e.g., Neo4j, Neptune) and vector databases (e.g., Milvus, OpenSearch).
- Experience with version control systems (e.g., Git) and Git workflows.
- Experience working with Azure DevOps (AzDO) boards for backlog management in Agile environments.
- Excellent analytical and problem-solving skills.
- Strong communication and collaboration abilities.
Preferred Skills:
- Experience with orchestration tools such as AWS Step Functions.
- Familiarity with data governance and compliance practices.
- Exposure to real-time data processing frameworks (e.g., Kafka, Spark Streaming).
- Knowledge of the NVIDIA ecosystem and its applications in data and AI.
- Exposure to RAPIDS libraries (cuDF, cuML, cuGraph) or CUDA-based tooling for GPU-accelerated data processing, enabling faster transformation and optimization during large-scale ingestion workflows.
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