Corporate Finance - VP - Portfolio Analytics

Responsible for the technical development, engineering and operational ownership of ADIC’s in-house liquidity model and supporting analytical applications. The role is primarily hands-on, focused on writing, optimising and maintaining production-grade code, building and operating data pipelines, and integrating ADIC’s data ecosystem (Snowflake, source systems, FactSet, internal databases) with the liquidity model. Partners with investment and support departments to deliver robust, reproducible analytics, reporting outputs and ad-hoc data extractions.

• Design, develop, test and maintain the code base of ADIC’s in-house liquidity model and related Python/SQL applications; own the full engineering lifecycle from requirements to deployment.

• Optimise, refactor and performance-tune existing Python and SQL code to improve execution speed, memory efficiency and maintainability of the liquidity model and analytical applications.

• Build, operate and monitor end-to-end data pipelines (ETL/ELT) integrating Snowflake with ADIC’s broader data ecosystem, including source investment systems, FactSet, and internal repositories that feed the liquidity model.

• Design and maintain database schemas, tables, views and stored procedures in Snowflake; write complex, performant SQL queries for transformation, reconciliation and analytics workloads.

• Implement automated data quality checks, reconciliation routines, logging and exception handling across the liquidity model’s data and compute layers; ensure production stability and auditability.

• Apply software engineering best practices including version control (Git), code review, modular design, testing and documentation; maintain a clean, production-ready code base.

• Deliver ad-hoc data extractions, queries and structured data packages on demand for internal stakeholders, including inputs for department semi-annual portfolio reviews, senior management meetings and board-level presentations.

• Automate recurring reporting outputs from the liquidity model and ensure reproducible, audit-ready analytical deliverables for executive and board consumption.

• Where relevant, contribute commentary on factors impacting liquidity, go-forward returns and nowcast scenarios, supporting the model’s analytical narrative (desirable, complementary to the core technical mandate).

• Ensure data engineering and modelling methodologies follow industry best practice and internal ADIC standards for security, data governance and reproducibility.

• Review code, data models and analytical outputs produced by the team and external vendors; enforce testing, documentation and release discipline.

• Proactively identify and implement automation, efficiency and reliability improvements across the liquidity model’s data, compute and reporting stack.

• Independently troubleshoot and resolve production issues across the application, pipeline and database layers, including root-cause analysis and permanent fixes.

• Work with investment and support staff, service partners and technology vendors (e.g. In516ht, Snowflake, FactSet) to resolve technical issues and deliver data and reporting requirements.

• Carry out other similar or related duties as assigned.

Requirements

Education

• University degree in Computer Science, Software Engineering, Data Science, Financial Engineering, Quantitative Finance, Mathematics or a related quantitative discipline

• Relevant technical certifications (e.g. Snowflake, AWS/Azure Data Engineering) or finance qualifications (CFA, CAIA) desirable but not required

Experience

• A minimum of 8 – 12 years of hands-on experience in data engineering, quantitative development or applied analytics, with at least 5 years building and maintaining production Python/SQL applications and data pipelines in a financial services or asset management setting.

• Proven track record engineering analytical applications for Asset Owners, sovereign wealth funds, or large multi-asset class asset managers, covering both public and private markets data.

• Core: Advanced Python skills, including production-grade application development, object-oriented design, unit testing, and experience with libraries such as pandas, NumPy, SQLAlchemy, and pytest. • Core: Deep, hands-on Snowflake expertise including complex SQL, stored procedures, views, performance tuning, role-based access, warehouses, streams and tasks. • Core: Proven experience building and operating data integrations across heterogeneous systems (databases, APIs, flat files) and connecting analytical/quant models to enterprise data platforms. • Nice-to-have: Familiarity with liquidity risk concepts and portfolio liquidity forecasting in investment portfolios (buyouts, VC, real assets, public markets, overlays). • Core: Strong working knowledge of Git-based version control, code review workflows, and CI/CD practices; comfortable collaborating on shared code bases. • Core: Experience with data pipeline orchestration and scheduling (e.g. Airflow, dbt, Snowflake Tasks, or equivalent), including dependency management and failure recovery. • Core: Solid grounding in relational data modelling, database design (star/snowflake schemas), and query optimisation; high level of accuracy and attention to detail in production code and data. • Nice-to-have: Familiarity with financial data platforms (FactSet, Bloomberg, PitchBook, MSCI Private Capital / Burgiss) and private-markets data structures (commitments, calls, distributions, NAVs). • Nice-to-have: Exposure to liquidity forecasting, cashflow modelling, and nowcast / scenario research methodologies for endowment-style portfolios; ability to translate analytical research into production code. • Strong written and verbal communication skills; able to translate technical work for senior management, board audiences and non-technical investment stakeholders; effective across cultural and functional backgrounds.