Principal Consultant - Lead Data Analyst

Principal Consultant - Lead Data Analyst / Data Modeller

Company: Transform Together

Contract: 6-month Fixed Term Contract, with option to extend or become a permanent member of the team

Compensation: OTE £90,000

Location: Hybrid / client-site as required

Technology environment: AWS, Databricks, Lakehouse architecture, complex relational databases, ETL/CDC pipelines

About Transform Together

Transform Together is a digital transformation consultancy helping organisations deliver business and technology change. We work with clients to bridge the gap between business ambition, operating model change and technology delivery.

We are growing our data and AI delivery capability and are looking for a Lead Data Analyst / Data Modeller to support the delivery of a strategic data platform within a complex financial services environment.

Role Overview

We are looking for a hands-on Lead Data Analyst / Data Modeller to define, build and govern canonical data models that will underpin a modern cloud-based data platform.

This is not a pure reporting analyst role. The role requires someone who can understand complex financial services data, translate operational processes and business logic into clear data requirements, and work closely with engineering and architecture teams to turn those requirements into scalable AWS and Databricks-based data products.

The successful candidate will be central to shaping the data foundation for multiple platform capabilities, including:

• Data extraction and ingestion from complex relational databases.
• Data quality, validation and exception management.
• Outbound client reporting and self-service reporting.
• Reconciliation and data movement processing.
• Automated analytical outputs and operational data products.

The core outcome of the role is to create a reusable, governed, canonical data model that standardises data across clients, schemes, source systems, processes and downstream outputs.

Key Responsibilities

Canonical Data Modelling

• Lead the design and development of canonical data models for a modern financial services data platform.
• Define conceptual, logical and physical data structures across key business entities, including clients, schemes, members, benefits, products, sources, transactions, movements, payroll, validation results and reporting outputs.
• Translate data from complex relational databases, operational systems, client files and third-party data sources into standardised canonical structures.
• Define source-to-target mappings, transformation rules, data definitions and data lineage.
• Ensure the canonical model supports downstream capabilities including validations, reporting, reconciliation, automated processing, analytics and future AI-enabled use cases.
• Work with Solution Architects and Data Engineers to ensure the model is implementable within AWS and Databricks architecture.

Data Analysis, Requirements and Quality Control

• Interpret complex operational processes, data flows, business logic and stakeholder needs.
• Convert business and operational requirements into clear data requirements, mapping documents, model specifications and acceptance criteria.
• Support workshops with business SMEs, technology stakeholders, architects and engineering teams.
• Challenge unclear or incomplete requirements and identify where business logic is hidden in spreadsheets, manual processes or individual SME knowledge.
• Analyse legacy data structures and identify standardisation, cleansing, transformation and remediation needs.
• Define data quality rules, validation logic and exception handling requirements.
• Support metadata-driven validation design, ensuring validation rules can be stored, maintained and audited against the canonical model.
• Analyse data quality issues and identify repeatable remediation patterns.
• Define data quality dashboards, exception reporting and data health metrics.
• Ensure validation outputs can support audit, regulatory assurance and operational sign-off.

ETL, CDC and Engineering Collaboration

• Work with Data Engineers to define ingestion requirements from complex relational databases, operational platforms and structured client files.
• Produce clear source-to-target mapping documents for ETL and CDC pipelines.
• Validate that engineering outputs align to the agreed canonical model and business rules.
• Support data reconciliation between source systems, staging layers, transformed data and reporting outputs.

Reporting, Reconciliation and Data Product Enablement

• Define the data structures required for outbound client reporting, including client-level, scheme-level, member-level and movement-based reporting.
• Support automated report data models for PDF, Excel, dashboards and client self-service outputs.
• Support reconciliation use cases by defining movement, current-position and exception-based data requirements.
• Support automation use cases by identifying data needed to populate operational tools, automate filtering and create reusable outputs.
• Ensure data models can support client-specific variations while avoiding excessive…