Data & Reporting Engineer - Building the Infrastructure Behind Client Reporting

About the Firm

We are a global investment management firm reimagining what the next-generation investment organization should look like.

As artificial intelligence, automation, data systems, and evolving market structure reshape the industry, we believe the traditional asset management operating model is due for reinvention.

We are investing aggressively in the people, infrastructure, workflows, and technologies that will define that future model — one that is more intelligent, adaptive, scalable, and integrated across investing, trading, operations, and decision-making.

Data and reporting sit at the operational core of that evolution.

The firm's ability to communicate performance, deliver institutional-grade client reporting, and act on information depends on the quality, speed, and integrity of our data infrastructure — a genuine competitive advantage.

This is an environment for builders, problem-solvers, and intellectually curious operators who want to help shape the future state of the investment industry.

We are seeking a Data & Reporting Engineer to own the full lifecycle of institutional client reporting — from data validation and pipeline management through the production and delivery of client-facing performance packages.

This role sits at the intersection of data engineering and institutional reporting.

You will work across our data platform and reporting infrastructure to ensure clients receive accurate, timely, well-structured performance information — and that the systems producing it keep improving.

The ideal candidate understands both the technical plumbing behind data and what that data ultimately communicates to a client.

You are equally comfortable writing a SQL validation query and redesigning a report layout that makes attribution easier to read.

You want to improve the process, not just execute it.

• Redesign and automate reporting workflows . Map monthly, quarterly, and annual cycles, identify manual steps and bottlenecks, and build auditable replacements in Python and SQL — questioning whether steps should exist at all, not just improving them in place.
• Build and maintain pipelines connecting source systems (portfolio, performance, risk) to client reporting outputs, with single-source-of-truth alignment across teams.
• Deploy AI-assisted tools to accelerate report production — data validation, exception-flagging, and narrative drafting.
• Own end-to-end production of client deliverables — performance attribution, account summaries, consultant reporting packages — accountable for accuracy and on-time delivery.