Senior Data Analytics
Parfois · Porto, Portugal
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CHALLENGE:
As a Lead Data Analyst at Parfois, you will lead the Data Analytics team within our Data Intelligence department, helping turn business needs into reliable data solutions that support decision-making across the company.
Working closely with business stakeholders and colleagues in Data Engineering and Data Science, you will translate business questions into analytical requirements, define suitable solutions, and guide their delivery and adoption.
This is a hands-on leadership role, combining team coordination and development with data analysis, data modelling, and reporting. You will help manage priorities, establish technical standards, and ensure that the team delivers accurate, scalable, and useful analytics solutions.
We are looking for someone with strong business understanding, technical expertise, and a collaborative approach, who enjoys developing people, solving problems, and continuously improving how the team works.
MAIN RESPONSIBILITIES:
- Lead, mentor, and support the Data Analytics team, encouraging autonomy, knowledge sharing, and continuous development.
- Work with business stakeholders to understand their needs, challenge requirements, and translate them into clear analytical solutions and deliverables.
- Coordinate the analytics backlog and delivery priorities with the Head of Data Intelligence, balancing business value, operational needs, and available capacity.
- Take ownership of analytics projects from requirements gathering and solution design through development, validation, deployment, and user adoption.
- Remain actively involved in data analysis and the development of Power BI reports, dashboards, and semantic models.
- Define and promote best practices for SQL, data modelling, DAX, report design, documentation, and performance optimisation.
- Ensure the accuracy, consistency, and reliability of reports and business metrics, working with stakeholders to establish common definitions.
- Collaborate with Data Engineering and Data Science to ensure that data and solutions meet business requirements.
- Oversee analytics operational support, coordinating issue resolution and addressing recurring problems.
- Support business functions across Parfois, including Sales, Buying, Distribution, Logistics, E-commerce, Finance, and Store Operations.
- Communicate findings and recommendations clearly, helping users understand and act on data.
- Promote self-service analytics and a strong data-driven culture across the organisation.
PROFILE
- Degree in Mathematics, Economics, Management, Engineering, Computer Science, or a related field.
- At least 5 years of experience in Data Analytics or Business Intelligence, including experience leading people or coordinating analytics delivery.
- Strong analytical skills and the ability to connect business processes, data, and performance indicators.
- Advanced SQL and Power BI skills, including data modelling, DAX, Power Query (M), and performance optimisation.
- Experience developing and maintaining Power BI semantic models and applying consistent reporting standards.
- Proficiency in Excel; experience with Power BI Paginated Reports is a plus.
- Experience managing stakeholder expectations, prioritising requests, and delivering analytics projects from requirements to adoption.
- Ability to coach colleagues, review technical work, and provide clear, constructive feedback.
- Experience working in Agile environments, using Azure DevOps or similar tools to manage backlogs and delivery.
- Familiarity with cloud data platforms, preferably Snowflake and/or Microsoft Fabric, is a plus.
- Knowledge of Python for data analysis and automation is a plus.
- Experience in retail or fashion, particularly in sales, stock, buying, distribution, or logistics, is an advantage.
- Strong communication and presentation skills, with the ability to explain complex topics to different audiences.
- A practical, organised, and collaborative approach, with sound judgement and a strong sense of ownership.
- Commitment to continuous learning, data quality, and delivering business value.
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