Data Engineer

Hamilton Lane•Conshohocken, PA

About The Position

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. 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. We are seeking a talented ETL Data Engineer with strong experience in Python and Azure Synapse Analytics 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 ETL data integration processes primarily using Python (PySpark), Azure Synapse Analytics Pipelines, and other Azure Synapse Analytics resources, ensuring the accuracy and availability of data for our analytical need. You will work closely with data scientists, analysts, and other stakeholders to deliver high-quality, well-organized data for insights and decision-making. If you are passionate about data, have a strong background in ETL processes, and are eager to work with state-of-the art Azure data solutions, we’d love to hear from you!

Requirements

Bachelor’s degree in Computer Science, Information Technology, or a related field; or equivalent work experience.
Proven experience in ETL data engineering with significant expertise in using Python (PySpark) to perform data extraction, transformation, and loading from REST APIs, SQL database tables, and CSV files.
Proficiency in using Azure Synapse Analytics resources including Notebooks, Pipelines, Linked Services, and Azure Key Vault.
Demonstrated ability to write complex SQL queries, optimize query performance, and work with both SparkSQL and MS SQL to effectively extract, transform, and load data.
Knowledge of data integration best practices and tools.
Experience with version control systems, such as Git (Azure DevOps).
Strong problem-solving and analytical skills, with a keen attention to detail.
Excellent communication skills, both verbal and written, with the ability to work collaboratively in a team environment with shifting priorities.

Nice To Haves

Certifications related to data engineering or data science (e.g. Azure Data Engineer).
Familiarity with big data technologies, machine learning, and data analysis.
Experience with data visualization tools (e.g. Power BI, Tableau).
Experience with Agile Methodologies.

Responsibilities

Develop and maintain ETL data engineering processes using Python (PySpark) within Azure Synapse Analytics Notebooks, and/or Azure Synapse Analytics Pipelines, to ensure efficient data extractions, transformation, and loading.
Apply expertise in data warehousing, understanding star schemas, facts, and dimensions, to design and build effective data storage structures in a Massively Parallel Processing (MPP) SWL Pool.
Extract data from various sources, including REST APIs, SWL database tables, and CSV files.
Utilize deep knowledge of Azure Synapse Analytics to design and optimize data notebooks/pipelines for scalability and performance.
Contribute to the implementation and understanding of other Data Fabric concepts, such as data lakes, lakehouses, delta lakes, and data cataloging, to enhance data management capabilities.
Collaborate with data architects to create data models and schemas that align with business requirements.
Implement data quality checks and validation processes to maintain data accuracy and consistency.
Identify and resolve performance bottlenecks and optimize ETL data notebooks/pipelines to meet SLAs.
Monitor ETL jobs, diagnose issues, and implement solutions to ensure data pipeline reliability.
Maintain comprehensive documentation of ETL data engineering processes, data flows, and data transformations.
Work closely with cross-functional teams to understand data requirements and provide support for data-related initiatives.
Ensure data security and compliance with data governance and privacy standards.