Data Analyst: Salesforce & Power BI

Data Analyst: Salesforce & Power BI

Hands-on analytical role · Data investigation and foundation-building

Outside IR35, 6-month contract, with a strong likelihood of extension.

About the Role:

This is a hands-on data investigation and foundation-building engagement, not just a reporting role. You'll get into the weeds of our data across Salesforce and connected systems, reconcile discrepancies, trace root causes, and establish a clean, trusted data layer that Power BI dashboards and advanced analytics can be built on.

We need someone who knows the Salesforce data model inside out and can turn a validated data layer into Power BI dashboards the business trusts. Experience with Oracle CPQ and ERP data is a strong advantage, but Salesforce and Power BI are the core of this role.

What you'll be doing:

• Audit and reconcile data across Salesforce CRM and connected CPQ/ERP systems to identify mismatches and inconsistencies.
• Perform root cause analysis on discrepancies, tracing issues back to process gaps, configuration errors, or integration failures.
• Analyse and clean product, customer, and account data to resolve duplication, incomplete records, and misclassification.
• Map data flows between systems and document where information breaks down or diverges.
• Highlight process and governance gaps that contribute to poor data quality.
• Build and deliver Power BI dashboards on a clean, validated data layer.
• Recommend ways to streamline master data across products, customers, and accounts.
• Work closely with business and technical stakeholders to align on data definitions and ownership.

Essential Skills:

• Hands-on Salesforce experience: data model, SOQL, objects and relationships. (Must Have)
• Power BI dashboard development: DAX, data modelling, published reports. (Must Have)
• Strong SQL: complex joins, CTEs, reconciliation queries.
• Root cause analysis and data lineage tracing.
• Ability to document findings clearly for both technical and business audiences.

Highly desirable:

• Oracle CPQ: configuration, pricing data, quote-to-order flows.
• ERP data experience (SAP, Oracle EBS, NetSuite, or similar).
• Understanding of how CRM, CPQ, and ERP interact in a quote-to-cash process.
• MDM (Master Data Management) concepts; data governance frameworks or data dictionaries.
• Data integration tools (MuleSoft, Boomi, Informatica).
• Python or R for data wrangling and analysis.
• Exposure to Snowflake, Azure, or Databricks.

The ideal candidate:

• Is comfortable with ambiguity, investigates first, then defines the problem.
• Can distinguish a data quality issue from a process design issue.
• Communicates findings proactively and turns analysis into actionable recommendations.
• Has done genuine data investigation and remediation, not just reporting.

Deliverables:

• Cross-system data reconciliation report with documented discrepancies and root causes.
• Prioritised list of data quality issues across products, customers, and accounts.
• Process gap assessment with recommendations.
• Cleaned and validated data layer ready for reporting.
• Power BI dashboards built on the validated data foundation.
• Data dictionary and field-mapping documentation across systems.