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
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.
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.
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.
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.
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.
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 bespoke build.
Produce and maintain data dictionaries, entity relationship models, lineage documentation, mapping specifications and data quality rule catalogues.
Document assumptions, unresolved questions, data risks and model design decisions.
Support architecture review and governance forums with clear data analysis evidence.
Ensure documentation is usable by engineering, QA, business stakeholders and future support teams.
Help establish data modelling standards and reusable templates for Transform Together’s wider data and AI delivery capability.
The Lead Data Analyst / Data Modeller will be expected to produce and own the following outputs:
Canonical data model.
Conceptual, logical and physical data model views.
Source-to-target mapping documents for complex relational databases and structured data sources.
Data dictionary and business glossary.
Data quality and validation rule catalogue.
Data lineage and transformation documentation.
Data requirements for ingestion, validation, reporting, reconciliation and automation capabilities.
Reporting data model and movement/reconciliation data structures.
Data acceptance criteria and test support documentation.
Data issue log, assumptions log and model decision log.
Handover documentation for engineering, QA and operational support.
Experience in financial services, pensions, insurance, wealth management, asset management or regulated data environments.
Strong experience as a Lead Data Analyst, Data Modeller or Senior Data Analyst on complex data platform delivery.
Proven experience designing canonical data models or enterprise logical data models.
Strong understanding of data modelling techniques, including conceptual, logical and physical modelling.
Strong SQL skills and ability to interrogate complex relational data structures.
Experience working with cloud data platforms, ideally AWS and Databricks.
Understanding of modern data architecture, data ingestion, ETL/ELT pipelines and CDC patterns.
Ability to work closely with Data Engineers, Solution Architects, QA, business SMEs and senior stakeholders.
Strong understanding of data quality, validation rules, reconciliation and exception management.
Experience working in Agile or hybrid delivery environments.
Excellent stakeholder management skills, with the ability to explain complex data topics to non-technical audiences.
Strong documentation discipline and attention to detail.
Experience working with complex relational databases and legacy operational platforms.
Experience with regulatory, client or operational reporting.
Experience supporting data migration, legacy system modernisation or platform replacement.
Experience with Databricks, Delta Lake, Spark SQL or PySpark.
Experience defining validation frameworks, data quality dashboards or metadata-driven rule engines.
Experience working in consultancy or client-facing delivery environments.
We are looking for someone who can operate with the pace, ownership and clarity expected in a consulting environment.
The right person will be:
Structured and analytical, with strong attention to detail.
Comfortable working with ambiguity and incomplete documentation.
Confident challenging business and technical stakeholders when requirements are unclear.
Collaborative, pragmatic and delivery-focused.
Able to bridge business, data and engineering teams.
Comfortable working at both detailed data-field level and wider platform-design level.
Proactive in identifying risks, assumptions and dependencies.
Clear in communication and able to simplify complexity.
Accountable for outcomes, not just analysis outputs.
