Basic knowledge of data querying and manipulation tools (e.g., SQL, MS Access); Familiarity with analytics tools (e.g., Python, R) and BI tools (e.g., Power BI, Tableau) is an advantage
Experience in data visualisation and dashboard development is preferred
Good written and verbal communication skills in English
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Ensure payroll and statutory reporting and payments are processed with high accuracy and timeliness according to our SLAs and local legal requirements.
Ensure data quality and data integrity for Payroll and HR data processes.
Receive, analyze, process and follow-up on personnel administration requests via multiple request channels, ensure accurate and timely interface to the respective internal/ external processing chains i.e. payroll, finance, data analytics, external service providers, local authorities etc.
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Administers and supports production environments hosted on Microsoft Azure, including virtual machines, storage, networking, containers, and Kubernetes services.
Troubleshoots application availability, performance, deployment, connectivity, container, Kubernetes, and Microsoft SQL-related issues.
Supports and troubleshoots automated application deployments, CI/CD pipelines, product upgrades, customer go-lives, and other planned cloud activities.
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Act as the primary coordinator between the Analytics Implementation Team, the Digital Content Team, and Business Stakeholders. Support and translate business requirements into technical tagging specifications for the external team, and translate technical data limitations or opportunities into business language for stakeholders.
Coordinate and manage end-to-end tagging strategies and SDK integrations by leveraging in mobile app analytics and Mobile Measurement Partners (e.g., AppsFlyer, Adjust, Branch) or platforms like Firebase; act as a technical bridge to ensure the Analytics Implementation team captures high-fidelity marketing insights while empowering the Digital Content team to effectively utilize tracking data.
Validate the data quality provided by external vendors before it reaches business reports. Ensure that tagging and SDKs are functioning correctly across all digital assets (pre-login, post-login, and mobile applications) to maintain trust in our data.
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Ensure payroll and statutory reporting and payments are processed with high accuracy and timeliness according to our SLAs and local legal requirements.
Ensure data quality and data integrity for Payroll and HR data processes.
Receive, analyze, process and follow-up on personnel administration requests via multiple request channels, ensure accurate and timely interface to the respective internal/ external processing chains i.e. payroll, finance, data analytics, external service providers, local authorities etc.
...
Act as the primary coordinator between the Analytics Implementation Team, the Digital Content Team, and Business Stakeholders. Support and translate business requirements into technical tagging specifications for the external team, and translate technical data limitations or opportunities into business language for stakeholders.
Coordinate and manage end-to-end tagging strategies and SDK integrations by leveraging in mobile app analytics and Mobile Measurement Partners (e.g., AppsFlyer, Adjust, Branch) or platforms like Firebase; act as a technical bridge to ensure the Analytics Implementation team captures high-fidelity marketing insights while empowering the Digital Content team to effectively utilize tracking data.
Validate the data quality provided by external vendors before it reaches business reports. Ensure that tagging and SDKs are functioning correctly across all digital assets (pre-login, post-login, and mobile applications) to maintain trust in our data.
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2 core SaaS products that are mature - actively used by customers & continuously evolving.
1 successful data product is already in place that provides market insights from the supply side. A new data product targeting the demand side is currently in its initial phase, with significant development and scaling planned for H2 2025 and into 2026
A combination of data engineering + statistical analytics
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Build and operate the harder engineering layers of a solution when projects demand it: orchestrated pipelines, worker-based and event-driven architectures, stream processing, and scaling for data volume and concurrency.
Own solution quality: data validation, model evaluation, reproducibility, performance, and operational reliability (monitoring, alerting, failure recovery).
Lead and mentor a team of junior data scientists and analysts; review their work, unblock them technically, and grow their skills.
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