THE ROLEWe 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.
KEY RESPONSIBILITIES- 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.
- Maintain reporting databases and reference tables, resolving discrepancies and enforcing data-governance standards.
- Design client report templates and presentation formats for clarity and usability.
- Partner with Investment, Product Management, Technology, and Compliance teams to gather requirements, fulfill custom client reporting requests, and coordinate cross-functional delivery.
QUALIFICATIONS- Experience in data, analytics, or reporting within financial services, asset management, or a similarly data-intensive environment.
- Hands-on proficiency in SQL and Python for data extraction, validation, transformation, and workflow automation, at production-grade level rather than ad hoc queries.
- Familiarity with investment data concepts (performance, attribution, positions, risk) and an understanding of how data integrity failures affect what clients see and how they interpret it.
- Comfort working across reporting and data visualization tools, including AI-native environments, and a bias toward adopting better approaches over established ones.
- Exposure to financial data platforms such as FactSet, Bloomberg, FIS, or similar, with comfort navigating the messiness of real-world data environments.
- Strong analytical mindset with the ability to identify inefficiencies, evaluate tradeoffs, and propose solutions rather than surface problems.
- Clear written and verbal communication, with the ability to translate technical findings into plain language for investment, operations, and client-facing stakeholders.
Experience with workflow orchestration, ETL tooling, or AI-assisted automation is a plus - but curiosity, adaptability, and a learning orientation matter more than any single technical skillset.
CULTURE & ENVIRONMENTWe operate in a high-accountability environment where the work is consequential. We value individuals who take ownership, communicate proactively, and follow through consistently.
We are particularly interested in people energized by change: those who want to rethink established processes, experiment thoughtfully with new technologies, and help shape how a modern investment organization operates.
This is not simply a maintenance role within an established framework. It is an opportunity to help build and evolve the future operating model of the firm.
