Implementation Specialist

Key Responsibilities

• Requirements and needs assessment: gather and analyse the customer's AML, fraud and screening requirements and advise on best-practice configuration.
• Configuration: translate policies, screening algorithms, regulatory requirements and risk factors into Hawk's setup, covering rules, screening, customer risk rating and workflows.
• Data mapping and integration: lead the technical deployment and integration with the customer's systems, via API or batch mechanisms.
• Testing and go-live: support the customer through testing, UAT and sign-off, troubleshoot issues, and support a successful transition to a production environment.
• Training and enablement: deliver training to the customer's teams from a functional as well as technical standpoint.
• Project management: plan and run the implementation from signature to go-live on time and within scope, with documentation and regular stakeholder updates, combining hands-on work with project leadership.
• Primary contact: be the first point of contact for implementation questions and issues.
• Collaboration and feedback: work closely with Customer Value Partners for a smooth implementation, and bring actionable customer feedback and gaps to Product, Development and Hawk's GTM organisation.
• Document every configuration decision and the reasoning behind it, so the setup holds up when the customer's auditor or regulator asks why a rule was set the way it was.
• Manage each implementation task and the implementation success as an outcome you own, as directed by our Customer Value Partner, resulting in activity completion and a go-live on the dates you committed to.

Your Profile

• 6+ years in implementation consulting or solution consulting, with at least 3 of those years hands-on implementing AML, fraud or screening solutions.
• Subject matter expertise in AML and compliance. You are familiar with key typologies and patterns, the regulatory expectations behind them, and how monitoring programmes as well as AI get examined.
• You can confidently stand in front of an MLRO and discuss and defend a rule set. This is a must-have, not a nice-to-have.
• Hands-on data skills: SQL, APIs, CSV and JSON, and data modelling. You're familiar with standardised payment message formats, including SWIFT MT and ISO 20022, SEPA and card data.
• Integration literacy. You don't write production code, but you're at home with APIs, JSON payloads, SFTP configurations as well as SSO.
• Proven track record of taking complex implementations to go-live on time, ideally several in parallel, with banking customers or other financial institutions.
• Stakeholder alignment. You ensure that internal and external parties both have the information they need, keep them aligned and manage expectations.
• Able to explain complexity simply. Imagine data models for compliance leads, fraud typologies for data engineers. You do it clearly in writing and in the room, in fluent English.
• A degree in computer science, information systems, engineering, finance or a related field, or equivalent practical experience.

Bonus

• Business-fluent German, for working with our DACH customers.
• CAMS (or equivalent) certification, or time spent in a second-line compliance or MLRO team.
• Builds constructive relationships with customers and stays calm and under pressure Builder mentality: works independently and brings structure to ambiguity where no established process exists, taking the initiative to define one.
• Is a resourceful problem-solver who investigates unknowns and tests system behaviour. Knows when and whom to escalate.
• Manages expectations proactively. Flags timeline and scope risks early, quantifies them, offers phased options and aligns Sales and Customer Success.
• Handles product gaps transparently. Clarifies the underlying (regulatory) requirement, offers workarounds with their trade-offs, feeds back to Product and doesn't commit to unconfirmed dates .
• You use AI tools such as Claude in your daily workflow to speed up mapping, documentation and analysis, and you have opinions about where they help and where they don't.

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Source: Statistisches Bundesamt (Destatis), table; dl-de/by-2-0; StarIn's calculation