Data Engineer
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Requirements
• Education & Certificates • A bachelor's degree or higher in a STEM field, required • Concentration in Computer Science, Math, Physics or other engineering related field, preferred • 5+ years of experience in data engineering or a related discipline, with a proven track record of success. • Experience in the financial services or private equity industry, preferred • Competencies & Attributes • Expertise in Python and SQL, with a strong foundation in data manipulation and analysis. • Proficient with Databricks/PySpark and dbt for data warehousing and data transformation tasks. • Experience with workflow orchestration tools e.g. Airflow, Temporal • Experience working with large language models (LLMs) especially prompt engineering, retrieval-augmented generation (RAG)s, and/or vector databases. • Knowledge of fundamental principles of machine learning, feature engineering, and knowledge graphs are pluses. • Demonstrated experience in designing and implementing complex data systems from the ground up. • Proficient in handling large-scale data projects, including data cleaning, ETL, and information retrieval. • Previous experience in a product development or financial services environment is highly desirable. • Excellent communication skills required, both verbal and written. • Nimble Gravity is a team of outdoor enthusiasts, adrenaline seekers, and experienced growth hackers. We love solving hard problems and believe the right data can transform and propel growth for any organization.
Responsibilities
• Build, scale, and maintain robust data solutions to support the firm's objectives. • Implement and optimize high-performance data pipelines: extraction, loading, transformation, and orchestration – that are designed for scalability, reliability, maintainability, and speed. • Lead software development projects end to end involving large language models (LLMs), retrieval-augmented generation (RAG) frameworks, and other AI technologies. • Champion modern software engineering practices as CI/CD, infrastructure-as-code, containerization, and cloud-native deployments • Collaborate closely with business stakeholders to transform use cases into production-ready services and solutions, owning the system from concept to production. • Implement rigorous testing and monitoring practices to maintain superior data quality and integrity. • Mentor and develop junior team members, fostering a culture of excellence and continuous learning within the team. • Be willing to travel up to 20% of the time to collaborate with distributed team members across locations.