Product Manager

Role Summary

The Product Manager - Market & Reference Data owns the product domain that acquires, normalises, enriches and distributes instrument, pricing and reference data across the Privé platform. This covers equities, fixed income, mutual funds, ETFs, indices, structured products and custom assets.

The role has three centres of gravity. First, it is the commercial and technical interface to Privés market data vendors: scoping which fields are required, requesting them through the correct contractual channel, interpreting licence terms, and resolving coverage gaps when a client or prospect presents a universe Privé does not yet cover. Second, it owns the product backlog for data ingestion, identifier management, asset lifecycle events and data-quality controls, writing specifications engineering can build against. Third, it is the escalation point when institutional clients dispute a data point, when an entitlement boundary is at risk, or when a scheduled feed fails.

This is a hands-on, evidence-driven product role. The successful candidate is expected to be equally comfortable reading a vendor data licence, debating whether yield-to-worst should be struck on mid or bid prices, and interrogating a failed import job to determine whether the fault sits with the vendor file, the parser, or the mapping.

Key Responsibilities

1. Market Data Vendor Management

• Own the day-to-day working relationship with Privés market data providers (Bloomberg, Morningstar, Xignite, Yield Book, WM Datenservice, index providers and regional sources), acting as the single point of coordination between vendor support/account teams and Privé engineering.

• Translate internal and client requirements into precise vendor field requests, specifying the exact data items, request/response file formats, delivery channel (SFTP bulk, API, on-demand fetch) and refresh frequency required.

• Read and interpret vendor agreements, schedules and data packages to determine what Privé is entitled to distribute, what is missing, and what would need to be added and translate this into a clear commercial ask.

• Run coverage assessments for prospects, PoCs and new client universes: take a client-supplied list of ISINs, tickers or funds, determine current coverage across existing providers, identify gaps, and advise on the fastest and cheapest route to close them.

• Obtain and pressure-test vendor quotations for incremental data packages, exchanges or asset lists, and present cost-versus-benefit options to the Head of PSI and the commercial team.

• Track vendor service quality: file delivery reliability, data completeness, response times on support tickets and escalate formally when standards are not met.

• Maintain an authoritative internal register of what data Privé licenses, from whom, under what restrictions, and which clients and modules consume it.

2. Instrument & Reference Data Product Ownership

• Own the product definition of the instrument model across asset classes - equity, fixed income, mutual funds, ETFs, indices, structured products and custom/private assets - including which attributes are mastered, from which source, and with what precedence.

• Define and maintain identifier management rules across ISIN, SEDOL, ticker/exchange, FIGI, vendor-proprietary IDs and internal keys, including ticker merges, historical identifier retention, and cross-vendor identifier lookup.

• Own asset-class-specific data logic and its correctness: bond metrics (yield-to-worst, coupon frequency and day-count conventions, call schedules, perpetual and callable structures, credit ratings and their source), fund attributes (share classes, NAV, distribution frequency, feeder-to-target fund linkage, holdings and breakdowns, fund documentation), equity attributes (corporate actions, split-adjusted price series, dividend record and pay dates), and index/benchmark configuration including composite and custom benchmarks.

• Own taxonomy and mapping standards: vendor category and sector schemes (e.g. GICS, BICS, vendor category IDs) mapped to Privé asset classes and sectors, country-to-region mappings, exchange reference data, and harmonisation of enumerated values such as credit rating scales across providers.

• Specify the behaviour of data ingestion pipelines: scheduled fetches, bulk imports, on-demand creation, correction files and back-population, including error handling, partial-failure semantics and retry policy.

• Own the roadmap for the platform's data services, including the ongoing migration of instrument and time-series data out of the monolith into dedicated services, and sequence it so that client commitments are not disrupted.

3. Data Licensing, Entitlements & Client Data Boundaries

• Own the product controls that enforce data entitlements: ensure that a data point sourced under one client's licence is never exposed to a client without that entitlement, at field, source and tenant level.

• Audit existing client environments against their contractual data entitlements, and specify remediation where boundaries are unclear or have drifted.

• Advise clients and internal stakeholders on whether a requested field can be served from an existing licence, requires a package upgrade, or must be sourced from an alternative provider.

• Support commercial teams during SOW and contract discussions by defining the market data section: what is included, what is client-provided, what carries an incremental cost, and what redistribution constraints apply.

• Ensure that data usage across production tenants is defensible in the event of a vendor audit.

4. Backlog Ownership & Delivery

• Own and groom the data-domain backlog in Jira, writing epics, stories and bugs with clear problem statements, requirements, acceptance criteria and priority rationale that engineering can estimate and build against without further interpretation.

• Partner with engineering leads on technical design trade-offs, data model changes, storage and time-series design, API and GraphQL contract changes, job scheduling, challenging estimates and sequencing where necessary.

• Produce effort and cost estimates for client-driven data requests, distinguishing platform capability from client-specific configuration, and feed these into commercial responses.

• Prioritise across a mixed load of new capability, client-committed enhancements, production defects and technical remediation, and hold the line when scope expands mid-flight.

• Validate delivered work through SIT and UAT, including data-level verification, before it reaches production.

• Maintain product documentation for data APIs, field definitions and import processes for both internal and client consumption.

5. Data Quality, Operations & Client Escalation

• Own data quality as a product outcome: define the controls, validations, monitoring and alerting that detect missing, stale, duplicated or incorrect data before a client does.

• Act as the escalation point for client-raised data disputes: investigate whether the root cause is vendor data, ingestion logic, mapping, configuration or user expectation, and communicate the finding and remediation clearly to institutional stakeholders.

• Monitor scheduled job health and feed reliability, and drive systematic fixes rather than repeated manual correction.

• Work with Data Operations to resolve reference data issues, duplicate assets, incorrectly classified instruments and universe discrepancies.

• Identify manual, repetitive data operations work and drive it toward automation and self-service tooling.

• Report on data domain health to the Head of PSI: open defects by severity, recurring themes, vendor issues, and coverage risk across the client base.

Requirements

Essential

• 4+ years in a product, business analysis, data management or market data role within financial services, capital markets, wealth management, a market data vendor, or a fintech serving those markets.

• Working knowledge of instrument data across multiple asset classes (equity, fixed income, mutual funds and ETFs) including the attributes that define each, how they are identified, and how their prices and events behave.

• Practical familiarity with at least one major market data provider (Bloomberg, Morningstar, Refinitiv/LSEG, FactSet, ICE, SIX, Xignite or similar), including how data is requested and delivered in practice.

• Comfort with structured data formats and delivery mechanisms: vendor request/response files, CSV and XML feeds, SFTP delivery, and REST APIs. Able to open a vendor file and reason about its contents.

• Demonstrated ability to read a commercial or licensing document and extract what it does and does not permit, and to identify what needs to be requested to close a gap.

• Experience writing structured requirements for engineering teams: user stories, acceptance criteria, data mappings and managing a backlog in Jira or equivalent.

• Analytical rigour: able to investigate a data discrepancy methodically and separate vendor issues from platform issues from user error.

• Excellent written and verbal English credible in front of institutional clients and vendor account teams.

• Comfortable in a small, fast-moving team where role boundaries are fluid and initiative is expected.

Desirable

• Direct experience negotiating or administering market data licences, entitlements or vendor cost management.

• Exposure to fixed income analytics in depth ie. yield conventions, day count, call schedules, credit rating hierarchies.

• Familiarity with corporate action processing and adjusted price time series.

• SQL or query experience (including document stores such as MongoDB) for independent data investigation.

• Experience with observability or monitoring tooling (New Relic, Grafana or similar) for feed and job health.

• Understanding of APAC market specifics, regional exchanges, local fund registration, and regional data sources.

• Mandarin or Cantonese for Greater China client and vendor interactions.

• Interest in applying AI and automation to data operations, mapping and quality control.