Responsibilities
- Design, build, and maintain scalable data pipelines and data workflows across a modern data environment.
- Develop and optimize data solutions using Snowflake, dbt, and Airflow.
- Build and enhance data lakes and data platforms, integrating data from a variety of internal and external sources.
- Develop reliable ETL/ELT processes to support reporting, analytics, and downstream applications.
- Establish data transformation and modeling frameworks using dbt.
- Develop and manage workflow orchestration using Apache Airflow.
- Optimize Snowflake environments for performance, scalability, reliability, and cost.
- Partner with data analysts, technology teams, and business stakeholders to understand requirements and translate them into effective data solutions.
- Implement data quality, monitoring, testing, and governance practices across pipelines and data products.
- Troubleshoot data pipeline and platform issues and proactively identify opportunities for improvement.
- Contribute to the ongoing evolution of the firm's data architecture and engineering standards.
- Clearly communicate technical solutions, project status, risks, and recommendations to both technical and non-technical audiences.
Qualifications
- 5+ years of experience in data engineering, data platform engineering, or a related discipline.
- Strong hands-on experience with Snowflake.
- Experience building and maintaining data pipelines using modern ETL/ELT technologies.
- Strong experience with dbt and data transformation/modeling.
- Experience with Apache Airflow or similar workflow orchestration platforms.
- Experience designing and building data lakes, data warehouses, and/or modern cloud data platforms.
- Strong SQL skills and experience working with large, complex datasets.
- Experience with Python or another programming language commonly used in data engineering.
- Understanding of data architecture, data modeling, data quality, and data governance principles.
- Ability to work effectively in a fast-paced, collaborative environment.
Preferred Qualifications
- Experience working within financial services, private equity, investment management, banking, hedge funds, or asset management.
- Experience working with financial or investment-related datasets.
- Exposure to cloud platforms such as AWS, Azure, or GCP.
- Experience with additional modern data technologies and cloud-based data platforms.
- Experience supporting data initiatives involving analytics, AI, or machine learning.