[Hiring] Business Intelligence Analyst @mindit.io

Role Description

We design and build data and BI solutions for clients across greenfield and brownfield engagements, from new data platforms and analytical solutions to the redesign, modernisation and migration of existing estates.

The Business Intelligence Analyst turns business needs into agreed and implementable data and BI solutions. The role covers the full path from understanding business processes and information needs, through requirements, KPI and business-rule definition, source-data analysis and data modelling, to mapping, validation and acceptance.

The role combines business analysis with hands-on data analysis and modelling. It requires someone who can understand how a business operates, challenge and clarify what it needs, investigate how that information exists in the underlying systems, and translate the findings into models and specifications that can be implemented and validated.

The role owns the business and data definition throughout the engagement, working closely with business stakeholders and the technical delivery team.

Responsibilities

• Lead workshops with business and technical stakeholders on business processes, information needs, reporting requirements and current pain points.
• Analyse how information is created, changed and consumed across business processes and systems.
• Identify the business entities, events, measures, dimensions and relationships that need to be represented in the data platform.
• Define functional and non-functional requirements for data and BI solutions.
• Define KPIs, metrics, dimensions and business rules, including calculation logic, scope, grain, aggregation and exceptions.
• Establish agreed business definitions with accountable business owners and maintain the business glossary.
• Resolve ambiguity and conflicting requirements and drive stakeholders towards an agreed outcome.
• Specify reports, dashboards and analytical requirements to a level that can be implemented by engineering and BI teams.
• Analyse existing reports, dashboards and semantic models to establish how reported numbers are calculated.
• Profile source data for structure, content, relationships, completeness and quality.
• Work with source-system owners and DBAs to interpret source data and understand how it is generated and maintained.
• Assess data quality and define the validation rules, thresholds and acceptance criteria.
• Develop conceptual and logical data models that represent the business concepts and information requirements.
• Develop physical models where required, including tables, keys, relationships, grain, historisation and physical design considerations.
• Apply dimensional, 3NF/canonical and Data Vault modelling approaches.
• Determine how business concepts should be represented across data warehouse/lakehouse layers and semantic models.
• Define historisation and as-was reporting requirements.
• Define record matching, deduplication and survivorship approaches across source systems.
• Ensure that the proposed models can answer the agreed business questions and support the required KPIs and reporting.
• Produce source-to-target mappings covering source attributes, transformations, business rules and target structures.
• Define reference-data mappings and cross-system code harmonisation where required.
• Maintain traceability between business requirements, data elements, source systems, transformations and delivered outputs.
• Work with Data Architects, Technical Leads, Data Engineers and BI Developers through implementation of the specification.
• Review delivered data, semantic models and reports against the agreed requirements, models, mappings and business definitions.
• Support reconciliation, user acceptance testing and business sign-off.
• Maintain and evolve requirements, definitions and models as understanding develops during delivery.
• Take key analysis, modelling and specification artefacts through review and stakeholder sign-off.

Qualifications

• 8+ years of experience working on data warehouse, BI, analytics or data platform programmes, with substantial responsibility for requirements analysis and data modelling.
• Experience working across the lifecycle of data platform initiatives, including assessment, design and delivery.
• Ability to take ownership of a business or data problem through to a specification an engineering team can implement.
• Experience working directly with business owners and senior stakeholders as well as architects, engineers and BI specialists.
• Stakeholder management and prioritisation, including resolving competing requirements and definitions.
• Experience working iteratively and adapting requirements and designs as understanding improves.
• Written and verbal communication with business and technical audiences.
• Use of AI tooling in day-to-day analysis, modelling and documentation, with responsibility for verifying the output.

Requirements

• Experience in business process analysis and requirements engineering.
• Understanding of KPI and metric definition and the business rules behind analytical reporting.
• Experience specifying reports, dashboards and semantic models.
• Experience analysing existing BI solutions to recover business logic and calculation rules.
• Experience defining non-functional requirements for data and BI solutions.
• Requirements traceability and specification-writing skills.
• Experience in entity relationship and logical data modelling.
• Experience with dimensional modelling and with 3NF/canonical modelling.
• Experience with physical data modelling, including DDL, keys, indexes and partitioning, and the performance and scalability implications of a model.
• Understanding of data warehouse and lakehouse layering, and of modelling semantic layers for BI platforms.
• Experience with historisation and as-was or point-in-time reporting.
• Experience with record matching, deduplication and survivorship across source systems.
• Understanding of modelling standards, naming conventions, keys, relationships and grain.
• Experience using a data modelling tool such as Erwin, ER/Studio or equivalent.
• Fluent SQL and data profiling skills.
• Ability to investigate source data and identify relationships, business rules, anomalies and data-quality issues.
• Experience profiling source systems where direct database access is unavailable, using client-run scripts, extracts or supplied data.
• Experience with BI semantic models, and with Power BI and DAX.
• Experience with source-to-target mapping, reconciliation and data validation.
• Understanding of how data quality, lineage and reconciliation should be incorporated into a data solution.
• Experience establishing and maintaining business definitions and glossary terms.
• Understanding of data ownership and accountability for business definitions.
• Understanding of data classification, including personal and sensitive data.
• Experience defining data quality rules and thresholds.
• Experience with reference data and cross-system code harmonisation.
• Understanding of how governance requirements affect data models, reporting and access.

Preferred Experience

• Experience with Data Vault modelling.
• Understanding of ingestion and transformation patterns on a modern data platform.
• Exposure to specifying requirements for machine learning and GenAI use cases is an advantage.
• Experience working in regulated or audit-bearing reporting environments.
• Experience working in a consulting environment with multiple clients and varied data estates.
• Experience across both assessment or design engagements and delivery engagements.