Senior Data Engineer
The ideal candidate is a hands-on engineer with strong experience across Snowflake, dbt, Apache Airflow, SQL, Python, and CI/CD who has previously worked within private equity, asset management, investment management, or broader financial services . This person should understand the importance of data quality, lineage, governance, and reliability when working with financial and investment data.
Key Responsibilities
- Design, build, and maintain scalable data pipelines and ETL/ELT workflows supporting investment and financial data.
- Develop and optimize modern data architecture leveraging Snowflake as the cloud data warehouse.
- Build reusable and well-tested data transformation models using dbt .
- Develop, schedule, and monitor complex data workflows and pipelines using Apache Airflow .
- Build integrations across internal and external data sources, including investment, portfolio, accounting, market, and third-party financial datasets.
- Write advanced SQL and Python for data ingestion, transformation, automation, and processing.
- Implement and maintain CI/CD pipelines for data engineering workflows, ensuring proper testing, version control, deployment, and release management.
- Partner with investment, finance, portfolio management, operations, and analytics teams to translate business requirements into scalable data solutions.
- Collaborate with BI and analytics teams to create reliable datasets that support dashboards, reporting, and investment decision-making.
- Establish best practices around data quality, testing, monitoring, lineage, governance, and documentation .
- Troubleshoot pipeline failures, performance issues, and data-quality problems across the data ecosystem.
- Help improve and modernize the firm's overall data platform as the organization continues to scale its use of data.
Required Qualifications
- 5+ years of professional Data Engineering experience, ideally within a sophisticated financial environment.
- Prior experience within private equity, asset management, investment management, hedge funds, real estate investment management, or financial services .
- Strong hands-on experience with Snowflake .
- Strong experience building transformation pipelines and data models using dbt .
- Production experience with Apache Airflow for workflow orchestration.
- Advanced SQL skills and strong proficiency with Python .
- Experience building and maintaining CI/CD pipelines for data platforms and data engineering applications.
- Strong understanding of ETL/ELT architecture, data warehousing, dimensional modeling, and modern cloud data platforms .
- Experience integrating data from multiple APIs, databases, files, SaaS applications, and third-party data providers.
- Familiarity with Git-based development workflows and automated testing/deployment.
- Strong communication skills with the ability to work directly with both technical teams and financial/business stakeholders.
Preferred Experience
- Experience working with data related to funds, investments, portfolios, transactions, valuations, performance, accounting, or financial reporting .
- Understanding of alternative investments such as private equity, private credit, real estate, or infrastructure .
- Experience building or contributing to enterprise data lakes / lakehouses / data warehouses .
- Exposure to platforms such as Databricks, AWS, Azure, Power BI, Sigma, iLevel, or similar financial/data technologies .
- Experience operating in an environment where data accuracy, auditability, and governance are critical.
Ideal Profile
This role is best suited for a hands-on Senior Data Engineer who can own complex pipelines from ingestion through transformation and delivery. The successful candidate will combine strong engineering fundamentals with an understanding of how data is consumed by investment, portfolio management, and finance teams .
They should be comfortable operating in a fast-paced investment environment, partnering directly with business stakeholders, and helping build a modern data platform that supports increasingly sophisticated analytics and reporting needs.