Senior Associate - Investment Data Engineer

This role is part of the Private Equity Enablement team, within the Fund Services group, with functional reporting into the Data Services team, which is responsible for the fund’s data management and data governance. This role combines private equity domain expertise with modern data engineering practices to deliver scalable, high-quality data solutions supporting portfolio monitoring, reporting, and investment insights. The role sits within the Private Equity Enablement function with strong alignment to Data Engineering, focusing on building and maintaining data pipelines, ensuring data quality, and operationalizing governance frameworks across investment data platforms. The Senior Associate will work closely with Data Services and the Private Equity team to build the PE data foundation.

Key Duties/ Position Requirements:

• Adhere to the and maintain high standards for data accuracy, quality, and governance.
• Provide ongoing support for data platform operations including design documentation, BAU issue triage and resolution, and other business readiness activities, including ETL processes and data quality functionalities.
• Responsible for ensuring any data exceptions and data quality issues are investigated and resolved on a timely basis.
• Own and enhance investment data workflows including ingestion, transformation, validation, and delivery across systems.
• Design, build, and maintain robust data pipelines and data models supporting private equity portfolio monitoring and reporting.
• Own the format and content of routine reporting across multiple internal and external platforms. (including changes facilitated within the i-Level portfolio system)
• Collaborate with business analysts, product owners and other stakeholders to ensure the completeness and accuracy of business requirements, data models, source-to-target mappings, and data quality control definitions.
• Develop and configure data platform components in alignment with business needs, including supporting metadata management within the enterprise data catalogue.
• Apply industry best practices for testing to perform comprehensive unit testing of developed components.
• Develop curated datasets, dashboards, and reporting outputs for portfolio monitoring, performance attribution, and analytics.
• Coordination with data services teams and other downstream stakeholders including monitoring functions
• Bridge technical and business perspectives by translating complex investment data and analytics into concise, decision‑focused insights for senior stakeholders
• Work with Data Engineering team in the delivery of data solutions
• Assist in the delivery of high-quality data solutions with the Data Engineering team
• Assist in building curated data packs, dashboards, and datasets for deal teams.

Qualifications:

• Experience working in complex enterprise Data Environments.
• Proven Engineering experience in Python, Spark, SQL and related languages.
• 2 years’ experience in designing and building advanced Data Pipelines and Data Warehouses at an enterprise scale.
• DevOps experience inclusive of creating CI/CD/CT pipelines for data engineering and analytics using Azure Devops.
• Experience in one or more of the following public Data cloud platforms, Azure Synapse, AWS Redshift, Google Cloud, with preference for Azure Cloud Synapse and ADF, ADB.
• Proficiency in ETL (Extract, Transform, Load) processes and tools.
• Good understanding of data management functions and usage of investment data in various forms.
• Capable to contribute to solving complex problems in a team setting.
• Experience with data / investment management platforms such as Finbourne – LUSID and Informatica, Bloomberg, Aladdin, S&P i-Level and Burgiss, Refinitiv and Bloomberg.
• Proficient in using tools and methodologies to ensure data consistency, accuracy, and security, aligning with Data Governance best practices.
• Experience in Private Markets.
• Database administration and management experience including but not limited to Microsoft SQL technologies.
• Hands-on experience with the agile development lifecycle, including the design and implementation of ETL components within a data platform. Ability to collaborate with development teams to review and enhance code quality, and to present technical solutions effectively to stakeholders.
• Proficiency in data profiling and testing, including the use of test data and execution of unit, integration, acceptance, and performance testing for data platforms.
• Knowledge of continuous integration/continuous delivery pipelines and DevOps practices for data platform deployment and maintenance.
• Understanding of financial markets and financial instruments across multiple asset classes
• DevOps and CI/CD: Git, Azure DevOps
• Workflow and documentation: ServiceNow, Jira, Confluence.