Role Description
We are seeking an experienced Data Acquisition Manager to be a part of our new Risk Intelligence Data Team and lead all aspects of our data supplier & sourcing domain. Responsibilities for this critical role include developing a clear data source acquisition strategy, managing 3rd party suppliers, automating the data acquisition pipeline, and establishing source management tooling & governance across our Active Data Layer programme. You will oversee the management and evolution of Risk Intelligence’s data supply chain. This includes working across various data acquisition methods, from 3rd party supplier selection and management, automation of data source monitoring and data extraction, and broader web search techniques. This role will involve working closely with Engineering, Product, and Operations, as well as maintaining good communications and engagement with a wider group of business stakeholders. The overall objective of the Head of Data Acquisition will be to maintain a comprehensive but cost‑effective data supply and library of sources covering Sanctions, PEPs, and Adverse media, that ultimately improves the accuracy of Risk Intelligence’s World Check screening solutions.
Key Responsibilities
- Develop and own the strategy for data acquisition, determining how best to access data sources needed to achieve data performance for screening customers.
- Together with engineering teams, develop tools, agents, and techniques for monitoring and acquiring data.
- Collaborate with other members of the Product Data Team to integrate data acquisition processes with data curation & enrichment orchestration.
Data Source Evaluation Framework
- Create a clear and logical framework for discovering and evaluating new sources of data. Work together with the RI Operations stakeholders to develop and implement new operating processes.
- Together with Data & Product stakeholders, create and implement a Data Improvement and ROI decision workflow that works agentically across the data source landscape.
Data Source Management
- Together with Data & Product stakeholders, create and implement a Data Improvement and ROI decision workflow that works agentically across the data source landscape.
Data Supplier Management
- Undertake vendor selection for new data suppliers and data sourcing partners as required.
- Design and implement a data vendor SLA policy which establishes quality controls, rights management, and data governance procedures.
Data Acquisition Budgeting
- Together with Data & Product stakeholders forecast and calculate data sourcing budgets matched to data coverage and screening performance taking into account the different areas of cost, such as Agent cost, data sources, 3rd party data purchase.
- Work with Product leads on application of budget against data acquisition activities, with a clear link to downstream effect of spend.
Required Experience & Qualifications
- Bachelor's, Master’s, or PhD (preferably in field such as Computer Science, Mathematics, Statistics, Financial Engineering, or equivalent).
- Significant professional experience (typically 7–12+ years) in relevant data roles, or related areas.
- Demonstrated success working with technical teams or senior specialists in data automation environments.
- Strong understanding of data acquisition techniques —including LLMs, crawlers, bot blocking, agentic architectures, managed services, and outsourcing.
- Hands‑on experience working with data suppliers and 3rd party providers of data, data automation services and solutions.
- Familiarity with data scraping, source management, and data automation technologies.
- Excellent communication skills, with the ability to present complex findings clearly to both technical and non‑technical audiences.
Preferred Experience & Qualifications
- Experience in B2B data or RegTech environments.
- Experience managing data teams, specifically data supply and acquisition teams and activities.
- Experience in working with data providers (BPOs, data aggregators), with specific reference to data within the Financial Crime and Regulation space.
- Experience with Generative AI and agent design / implementation.
- Experience of data lineage requirements and compliance / audit frameworks.
What Success Looks Like
- All data sources documented and monitored for core KPIs and meta information.
- Rolling assessment and improvement of data sources based on value, accessibility, and coverage.
- Rapid source evaluation and implementation process for new / alternative sources.
- Stakeholders across engineering, operations, and product view the function as a strategic partner and trusted authority.
Equal Opportunity Statement
We are proud to be an equal opportunities employer. This means that we do not discriminate on the basis of anyone’s race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law. Conforming with applicable law, we can reasonably accommodate applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.
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