Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems
Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems
Create and contribute to frameworks that improve the efficacy of logging data, while working with data infrastructure to triage issues and resolve
Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way
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Support reporting, analysis, and optimisation activities across enterprise information protection technologies such as Proofpoint, Symantec DLP, and Microsoft Purview.
Analyse operational patterns, policy effectiveness, and protection outcomes.
Support policy tuning initiatives and continuous improvement activities.
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Oversee Operations of the Data Platform - Provide leadership and oversight for the daily operations of the enterprise data lake and data warehouse. Ensure efficient data ingestion, processing, and monitoring practices are in place while delegating operational tasks to the team.
Guide Design and Development of ETL/ELT Pipelines - Lead the architectural direction and standards for ETL/ELT processes integrating data from multiple sources into the Data Lake, Data Marts, and Data Warehouse. Review team deliverables to ensure scalability, efficiency, and adherence to best practices.
Engage Stakeholders and Align Business Requirements - Collaborate with business units to gather and prioritize requirements. Translate these into technical specifications and oversee the delivery of data solutions that enable decision-making and improve operational efficiency.
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Oversee Operations of the Data Platform - Provide leadership and oversight for the daily operations of the enterprise data lake and data warehouse. Ensure efficient data ingestion, processing, and monitoring practices are in place while delegating operational tasks to the team.
Guide Design and Development of ETL/ELT Pipelines - Lead the architectural direction and standards for ETL/ELT processes integrating data from multiple sources into the Data Lake, Data Marts, and Data Warehouse. Review team deliverables to ensure scalability, efficiency, and adherence to best practices.
Engage Stakeholders and Align Business Requirements - Collaborate with business units to gather and prioritize requirements. Translate these into technical specifications and oversee the delivery of data solutions that enable decision-making and improve operational efficiency.
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Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems
Create and contribute to frameworks that improve the efficacy of logging data, while working with data infrastructure to triage issues and resolve
Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way
...
Partner with business stakeholders across various functions to understand their challenges, identify opportunities, and translate them into data-driven solutions that create tangible value.
Analyse large and complex datasets to uncover trends, customer behaviours, business opportunities, and potential risks that inform key business strategies.
Apply statistical analysis, predictive modelling, and machine learning techniques to address business problems and generate forward-looking insights for decision makers.
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Terms of employment / engagement shall be subject to contract and local applicable laws
Own a real-world project stream end-to-end, such as syndicate detection, user behavior sequencing, market manipulation detection, or feature discovery.
Process and analyze billions of records using PySpark, Hive, and Trino, and develop robust SQL pipelines for feature engineering and validation.
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Terms of employment / engagement shall be subject to contract and local applicable laws
Own a real-world project stream end-to-end, such as syndicate detection, user behavior sequencing, market manipulation detection, or feature discovery.
Process and analyze billions of records using PySpark, Hive, and Trino, and develop robust SQL pipelines for feature engineering and validation.
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You will partner with product, design, and engineering teams to derive insights, design experiments, and drive key product decisions.
You will support strategic plans with data insights, performing deep-dives into user behaviour, segmented views and product efficacy.
You will provide tailored analysis for specific products and strategies, define critical business and product metrics, and make regular recommendations for continuous improvements.
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Drive end-to-end business analysis across data management, Microsoft Azure-based data services, analytics, Power BI and Gen AI initiatives, translating business needs into clear and actionable requirements that enable successful delivery.
Engage business stakeholders to define scope, clarify objectives and map current-state processes, creating the foundation for aligned and achievable delivery outcomes. Lead workshops, interviews and solution-design discussions that unite business users, product owners, data engineers, architects and vendors, driving meaningful cross-functional alignment.
Produce high-quality documentation including business requirements, functional specifications, user stories, acceptance criteria, process flows and decision logs that serve as the single source of truth for delivery teams.
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You will partner with product, design, and engineering teams to derive insights, design experiments, and drive key product decisions.
You will support strategic plans with data insights, performing deep-dives into user behaviour, segmented views and product efficacy.
You will provide tailored analysis for specific products and strategies, define critical business and product metrics, and make regular recommendations for continuous improvements.
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Develop, maintain, and enhance analytical datasets, data models, and transformation workflows supporting investment and operational reporting requirements.
Support the ingestion, integration, validation, and documentation of investment and business data while ensuring datasets are scalable, reusable, and aligned with analytical standards.
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