Senior Data Engineer

Job description

**Job Requirements** At Quest Global, it’s not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better place—to make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster. **Job 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. **Key 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. We are known for our extraordinary people who make the impossible possible every day. Questians are driven by hunger, humility, and aspiration. We believe that our company culture is the key to our ability to make a true difference in every industry we reach. Our teams regularly invest time and dedicated effort into internal culture work, ensuring that all voices are heard. We wholeheartedly believe in the diversity of thought that comes with fostering a culture rooted in respect, where everyone belongs, is valued, and feels inspired to share their ideas. We know embracing our unique differences makes us better, and that solving the worlds hardest engineering problems requires diverse ideas, perspectives, and backgrounds. We shine the brightest when we tap into the many dimensions that thrive across over 21,000 difference-makers in our workplace. **Work Experience** **Required Qualifications** - 7-10 years of experience in data engineering or related roles. - 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, Amazon 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. - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. **Nice To Have Skills** - 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. **Preferred Qualifications** - 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).