ETL Data Engineer (Python & Snowflake)
Join Hamilton Lane , a global leader in private markets, as we scale to meet the demands of our growing client base. Here, you’ll work with ambitious, high‑performing teams built on integrity, candor and collaboration, backed by our market-leading data and technology. We invest heavily in our people and our partners, giving you the platform to enrich lives, safeguard futures and grow your career.
What We Do
As one of the largest private markets investment firms globally, we provide innovative solutions to institutional and private wealth investors around the world, specializing in flexibility and full-spectrum access. We currently employ approximately 800 professionals operating in offices throughout North America, Europe, Asia Pacific and the Middle East, and have $1.0 trillion in assets under management and supervision, composed of $146.1 billion in discretionary assets and $871.5 billion in non-discretionary assets, as of December 31, 2025.
The Opportunity:
We are seeking a talented ETL Data Engineer with strong experience in Python and Snowflake to join our dynamic team.
As an ETL Data Engineer, you will play a critical role in our expanding data engineering team. You will be responsible for designing, developing, and maintaining scalable data integration solutions primarily using Python (PySpark), Snowflake, and modern cloud data platform technologies, ensuring the accuracy, reliability, and availability of data for analytics and business decision-making.
You will work closely with data architects, data scientists, analysts, and business stakeholders to deliver high-quality, well-structured data products that support advanced analytics, reporting, and operational use cases.
If you are passionate about data engineering, enjoy building modern cloud data platforms, and are eager to leverage Snowflake's capabilities to drive business value, we'd love to hear from you.
Your responsibilities will be to:
Data Engineering & ETL Development
- Design, develop, and maitain scalable ETL/ELT data pipelines using Python (PySpark), Snowflake, and cloud-native integration technologies.
- Build reliable, efficient, and reusable data ingestion, transformation, and loading processes to support enterprise analytics and reporting needs.
Snowflake Data Platform
- Utilize Snowflake's architecture and capabilities to design, build, and optimize modern cloud data solutions.
- Implement and manage Snowflake objects including databases, schemas, tables, views, streams, tasks, stages, and stored procedures.
- Leverage Snowflake features such as virtual warehouses, data sharing, time travel, and automated scaling to maximize performance and cost efficiency.
Data Warehousing
- Apply expertise in dimensional modeling, star schemas, facts, and dimensions to design and implement scalable enterprise data warehouse solutions within Snowflake.
- Develop data models that balance business requirements, performance, and maintainability.
Data Source Integration
- Extract and ingest data from a variety of sources including REST APIs, relational databases, SaaS applications, flat files, and cloud storage platforms.
- Develop and maintain robust ingestion frameworks supporting structured and semi-structured data formats.
Cloud Data Architecture
- Contribute to the design and implementation of modern data platform concepts including data lakes, lakehouses, data mesh architectures, and enterprise data catalogs.
- Support integration between Snowflake and cloud-native services across Azure and other cloud platforms.
Data Modeling & Design
- Collaborate with data architects and business stakeholders to develop logical and physical data models aligned with business objectives.
- Establish and enforce data engineering standards and best practices.
Data Quality & Governance
- Implement automated data quality controls, validation frameworks, and monitoring processes to ensure accuracy, consistency, and completeness.
- Support data