Data Engineer responsible for developing data solutions and integrating systems for advanced analytics at Lilly. Focusing on data pipelines and solutions ensuring data quality and compliance.
About the role
- Senior Data Architect responsible for building data infrastructure at Trexquant, integrating diverse datasets for research and simulation applications. Collaborating with teams to enhance data accessibility and quality.
Responsibilities
- Architect and implement a unified data platform that integrates hundreds of vendor datasets, providing consistent, accessible, and high-quality data to simulators and researchers.**
- Design efficient storage and retrieval systems to support both large-scale historical backtesting and high-frequency research workflows.**
- Develop intuitive researcher interfaces and APIs that allow users to easily discover variables, explore metadata, and assemble data into standardized stocks × values matrices for rapid hypothesis testing.**
- Collaborate closely with quantitative researchers and simulation teams to understand their workflows, ensuring the data platform meets real-world analytical and performance needs.**
- Establish best practices for data modeling, normalization, versioning, and quality control across asset classes and data vendors.**
- Work with infrastructure and DevOps teams to optimize data pipelines, caching, and distributed storage for scalability and reliability.**
- Prototype and deploy internal data applications that enhance research productivity and data transparency.**
- Mentor and guide data engineers to maintain robust, maintainable, and well-documented data systems.**
Requirements
- 7+ years of experience in data architecture, quantitative research infrastructure, or large-scale data engineering in a financial or research-driven environment.**
- Proven experience designing and implementing scalable data storage solutions (e.g., columnar databases, time-series systems, object stores, or data lakes).**
- Strong proficiency in Python and familiarity with modern data stack technologies (e.g., Parquet, Arrow, Spark, SQL/NoSQL, distributed file systems).**
- Deep understanding of time-series and financial data modeling, including handling multiple vendors, instruments, and frequencies.**
- Experience building data interfaces, APIs, or tools that serve researchers, data scientists, or quantitative analysts.**
- Ability to translate research needs into efficient data schemas and access patterns.****
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, Mathematics, or a related quantitative field.**
- Strong collaboration, communication, and documentation skills.**
- Familiarity with cloud-based architectures (e.g., AWS, GCP, Azure) and modern data governance practices is a plus.
Benefits
- Competitive salary plus bonus based on individual and company performance.**
- Collaborative, casual, and friendly work environment.**
- PPO health, dental, and vision insurance premiums fully covered for you and your dependents.**
- Pre-tax commuter benefits.**
- Weekly company meals.
Job title
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