Role: Functional Data Analyst

Location: Bangalore/Coimbatore — In office

Role Summary

We are seeking a client-facing Functional Data Analyst to bridge business needs, customer outcomes, data-product requirements, and technical data solutions.

Working under the joint direction of the Enterprise Architect and the Product & Customer Success Lead, this individual will collaborate with customers, product owners, business stakeholders, architects, data and AI product teams, and engineering teams. The role will support data modernization, integration, migration, reporting, analytics, AI-enabled solutions, data governance, and custom software initiatives.

The ideal candidate combines strong business and functional analysis capabilities with practical data-analysis skills, including SQL, data profiling, source-to-target mapping, data validation, reporting, data-quality analysis, functional data modeling, and data-governance practices. The individual must also be able to identify data, analytics, and AI opportunities and translate them into clearly defined, business-focused requirements.

Key Responsibilities

Business and Functional Analysis

- Lead customer and stakeholder discovery sessions to understand business processes, data challenges, reporting requirements, decision-making needs, and desired outcomes.
- Translate business needs into functional requirements, data requirements, user stories, acceptance criteria, process flows, and use cases.
- Document business rules, calculations, KPIs, data definitions, reporting requirements, assumptions, and system dependencies.
- Identify requirement gaps, risks, constraints, and dependencies early in the project lifecycle.
- Maintain requirements traceability from discovery and solution design through development, testing, deployment, and customer acceptance.
- Support product owners with backlog creation, refinement, prioritization, and sprint planning.
- Ensure that functional and data requirements are clear, testable, and understood by technical delivery teams.

Data Product and AI Use-Case Definition

- Define business and functional requirements for data products, including intended users, business outcomes, data inputs, capabilities, KPIs, controls, and acceptance criteria.
- Identify opportunities to create or enhance data-driven product capabilities that improve customer operations, decision-making, and business outcomes.
- Translate business problems into actionable reporting, analytics, automation, and AI use cases.
- Define analytical requirements beyond traditional reporting, including trends, predictions, recommendations, exceptions, and decision-support needs.
- Consider data-product lifecycle requirements, including discovery, design, development, adoption, measurement, enhancement, governance, and retirement.
- Collaborate with AI and Data Product teams to assess business value, feasibility, data readiness, dependencies, risks, and success measures.
- Help prioritize data, analytics, and AI use cases based on customer value, strategic alignment, data availability, implementation effort, and risk.

Data Analysis and Solution Definition

- Analyze data across multiple source systems to identify patterns, relationships, inconsistencies, duplication, gaps, and quality issues.
- Write SQL queries to profile, validate, reconcile, and troubleshoot data.
- Create and maintain source-to-target mappings, data dictionaries,



transformation rules, field definitions, and business glossaries.
- Partner with the Enterprise Architect, data architects, and engineering teams to define data models, integration requirements, migration rules, and ETL/ELT requirements.
- Review entity-relationship diagrams, logical data models, interfaces, APIs, and proposed solution designs from a functional perspective.
- Ensure proposed solutions align with business requirements, enterprise architecture standards, data-governance expectations, and long-term customer objectives.
- Investigate data-related problems and support root-cause analysis and resolution.

Functional Data Modeling

- Define functional data models that accurately represent business processes, concepts, and information needs.
- Identify and document core business entities, attributes, relationships, and business rules.
- Define data hierarchies, classifications, reference structures, and business-oriented data organization.
- Collaborate with architects and data engineers to translate business models into conceptual, logical, and physical data designs.
- Review proposed data structures to confirm that they support reporting, analytics, integration, migration, operational, and AI requirements.
- Maintain alignment between business terminology, functional data models, source-to-target mappings, and implemented solutions.

Reporting and Analytics

- Work with customers and business stakeholders to define dashboards, reports, metrics, KPIs, and broader analytical requirements.
- Document calculations, filters, dimensions, drill-down requirements, data hierarchies, and reporting logic.
- Identify opportunities to use data for operational improvements, forecasting, trend analysis, anomaly detection, and improved decision-making.
- Collaborate with BI developers, data engineers, data scientists, and product teams to ensure analytical solutions use reliable and clearly defined data.
- Validate that reports, dashboards, calculations, and analytical outputs accurately reflect business expectations.
- Present data findings, trends, risks, and recommendations in a clear, business-focused manner.

AI/ML Data and Insight Validation

- Define business and data requirements for AI/ML use cases, including required data inputs, quality thresholds, business rules, expected outputs, and acceptance criteria.
- Assess how data completeness, accuracy, consistency, timeliness, relevance, and bias may affect AI/ML outcomes.
- Collaborate with AI, data science, product, and engineering teams to identify data-readiness gaps and remediation needs.
- Validate AI-generated insights, predictions, recommendations, and exceptions from a business and functional perspective.
- Confirm that AI-enabled outputs are understandable, relevant, traceable, and appropriate for the intended business decision or workflow.
- Document business feedback, limitations, exceptions, and improvement opportunities for AI-enabled capabilities.

Testing and Customer Acceptance





- Develop functional test scenarios, data-validation queries, expected results, and acceptance criteria.
- Validate data pipelines, integrations, migrations, reports, dashboards, analytical solutions, AI-enabled capabilities, and transformation logic.
- Perform pre- and post-migration data reconciliation.
- Coordinate functional testing and user acceptance testing with customers and internal teams.
- Document defects, assess business impact, support root-cause analysis, and verify corrective actions.
- Support deployment readiness, production validation, and post-release stabilization.
- Confirm that delivered functionality satisfies approved business, data, governance, analytics, and AI requirements.

Stakeholder and Delivery Collaboration

- Serve as a liaison among customers, product teams, customer success, architects, developers, data engineers, data scientists, QA, and BI teams.
- Facilitate requirements workshops, data reviews, solution-design discussions, requirement walkthroughs, and customer demonstrations.
- Communicate requirements, decisions, risks, dependencies, and project impacts clearly to technical and nontechnical stakeholders.
- Support discovery-to-delivery handoffs and ensure delivery teams have sufficient functional, data, governance, analytics, and AI context.
- Participate in Agile ceremonies, project-governance meetings, customer status discussions, and delivery reviews.
- Help ensure that delivered solutions create measurable business value and positive customer outcomes.

Required Qualifications

- Bachelor’s degree in Information Systems, Computer Science, Engineering, Business Analytics, or a related field.
- 4+ years of experience as a Functional Data Analyst, Data Analyst, Business Analyst, Functional Analyst, or in a similar role.
- Experience supporting data-warehouse, data-migration, system-integration, analytics, reporting, data-product, or data-modernization projects.
- Strong SQL skills and experience working with relational databases.
- Experience performing data profiling, validation, reconciliation, and root-cause analysis.
- Experience creating source-to-target mappings, data dictionaries, business glossaries, business rules, process flows, user stories, and acceptance criteria.
- Experience defining functional or conceptual data models, business entities and relationships, data hierarchies, and business-oriented data structures.
- Working knowledge of data lineage, metadata, master data, reference data, data ownership, and data-governance concepts.
- Ability to translate business problems into reporting, analytics, automation, and AI use cases.
- Understanding of how data quality, availability, relevance, and bias can affect analytics and AI/ML outcomes.
- Understanding of ETL/ELT processes, APIs, system integrations, data models, and data-quality concepts.
- Experience with reporting or visualization platforms such as Power BI, Tableau, or Looker.
- Experience working in Agile delivery environments and using tools such as Jira, Confluence, or Azure DevOps.
- Strong analytical, documentation, facilitation, and problem-solving skills.
- Strong written and verbal communication skills.
- Ability to explain technical, data, analytics, and AI concepts to business stakeholders and customers.
- Ability to manage multiple priorities in a fast-paced, client-facing environment.

📌 Functional Data Analyst (Bengaluru)
🏢 Aezion
📍 Bengaluru