Senior Business Analyst (Data & Analytics)

At hubQuest, we build and scale advanced data and analytics hubs for global organizations. We partner with enterprise clients to design, launch, and evolve core data capabilities, enabling scalable analytics, advanced use cases, and data-driven decision-making across regions. We are currently looking for a Senior Business Analyst (Data & Analytics) to support a global FMCG organization in strengthening its enterprise data foundations. The role focuses on building and evolving standardised, scalable data pipelines and datasets that serve as the backbone for analytics and data products across multiple markets.

• 5+ years of experience as a Business Analyst in data platform, data engineering, or analytics foundation initiatives
• Proven experience working with enterprise-scale data pipelines and datasets
• Strong ability to collaborate with Data Engineers and understand technical trade-offs
• Excellent stakeholder management and communication skills
• Experience coordinating work across multiple business and technical stakeholders
• Ability to manage timelines, dependencies, risks, and delivery expectations in complex environments
• Ability to explain complex data, platform, and delivery topics to non-technical audiences
• Hands-on experience with SQL and Excel for data validation and exploration
• Experience working in Agile delivery environments
• Fluent English

Nice to have:

• Experience in FMCG or similarly complex, data-intensive industries
• Experience working with multiple markets, regions, OpCos, or distributed stakeholder groups
• Exposure to cloud-based data platforms, e.g. Azure, AWS, GCP
• Understanding of data governance, lineage, and master data concepts
• Familiarity with data modelling approaches, e.g. dimensional, domain-oriented, canonical models
• Exposure to Python or data orchestration tools
• Experience contributing to cross-domain data standardisation initiatives
• Previous exposure to project management, delivery management, or PM/BA hybrid responsibilities

Business-to-data translation at platform level:

• Partner with business and data stakeholders to understand core processes and translate them into robust, scalable data requirements for enterprise data pipelines.

Definition of harmonised data structures:

• Lead the definition of common data concepts, metrics, and logic across markets, balancing global standards with local operational realities.

End-to-end ownership of data requirements:

• Own requirements for data ingestion, transformation, validation, and exposure, ensuring clarity, feasibility, and long-term sustainability.

Delivery coordination, timelines & dependencies:

• Support delivery planning by helping manage timelines, milestones, dependencies, and sequencing of work across data engineering teams, OpCos, and business stakeholders.

Risk and issue management across stakeholders:

• Proactively identify delivery risks, data readiness issues, dependency gaps, and stakeholder misalignments, helping teams make informed decisions and unblock progress.

Close collaboration with data engineering teams:

• Work hands-on with Data Engineers to shape pipeline logic, data models, quality rules, and dependencies across upstream and downstream systems.

Decision-making in complex environments:

• Support prioritisation and trade-off decisions related to data availability, quality, latency, standardisation, timelines, and business impact across multiple regions and source systems.

Agile delivery & coordination:

• Contribute to backlog ownership, sprint planning, delivery tracking, and coordination within agile squads focused on data platform development.

Stakeholder and OpCo alignment:

• Work closely with global stakeholders, local OpCos, business representatives, and technical teams to ensure shared understanding of scope, priorities, timelines, risks, and expected outcomes.

Data quality & governance leadership:

• Embed data quality, lineage, and governance considerations into pipeline design, proactively identifying risks and issues.