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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Finance Data Platform Consultant - **Company:** Accenture - **Location:** Manchester, UK - **Salary:** £61,767.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Python (Programming Language), Oracle (Applications), Standard Sql, Azure Machine Learning, SAP (Applications), Snowflake, Palantir Foundry, Change Data Capture, Data Management, Databricks - **Published:** October 7, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5916584030 ## About the Role You take ownership and follow through. You work well in a team and build trusted relationships with clients and colleagues. You're comfortable with ambiguity in complex organisations, bring good judgement and critical thinking to problems that aren't fully defined, and you're clear and considered in how you explain your reasoning. You're early in your trajectory and set on building the rest. Things that set strong candidates apart: hands-on experience with a modern cloud data platform, such as Snowflake, Databricks or Palantir Foundry, among others; experience moving financial data out of an ERP such as SAP or Oracle and into a reporting or analytics environment, and reconciling it once it is there; experience building management reporting or analytics on top of platform data; working knowledge of SQL, APIs and modern data-integration technologies, and some exposure to Python or ML platforms; an understanding of chart-of-accounts, enterprise structure and finance master data, and why it shapes everything downstream; and relevant cloud-data or AI certifications, or a professional finance or accounting qualification. If you have most of this and the drive to build the rest, we want to hear from you. Tell us what you'd bring. ## Description Working on the finance data platform. Hands-on experience of a modern cloud data platform (Snowflake, Databricks or Palantir Foundry, or something comparable) and a working grounding in how one is put together: the layers that take data from raw ingestion through a conformed core to the curated sets finance consumes, the data products built on top of them, and the access and security rules around them. You build and test what the design calls for. You write queries and transformations that are efficient as well as correct, because compute on these platforms is a cost the client pays for, and you can explain why a piece of finance data sits where it does. Integration with the ERP estate. Financial data originates in the ERP and the applications around it, and the integration is where most of the difficulty sits. You'll build and test the connections into that estate (SAP, Oracle or whatever a client runs) across subledgers and the general ledger, consolidation, close and reconciliation tooling. You work with ETL, ELT and API patterns, with both scheduled batch loads and change-data-capture feeds, and you handle the things source systems do in practice: master data that changes underneath you, late postings, restatements and reopened periods. You take reconciliation seriously. What the platform reports has to agree with what the ledger says, and building the tests and monitoring that prove it is often your job, as is catching a broken feed before it reaches a reporting pack. Enterprise structure and finance master data. An awareness of how a finance data model reflects the legal, management and reporting structure of the enterprise: chart of accounts, entities and ledgers, cost and profit centre hierarchies, and the finance master data underneath them. The same work runs on ERP programmes, where chart-of-accounts and finance master data design are core workstreams, so this is capability you can put to use on a platform build and on an ERP implementation alike. You'll work within the governance and lineage the design sets, document what you build, and keep a figure in a report traceable back to the transaction behind it. Analytics and reporting. This is what the platform is there to serve, and a good part of your work will sit here: building management reporting, the datasets behind self-service analytics, and the semantic and metrics definitions underneath both, so a measure such as margin or cost to serve means the same thing in every report that uses it. You'll build and test those models and reports, check the numbers behind them against the ledger, and work with the finance people who use them on what they need. AI on the platform. This is where financial data turns into decisions: predictive forecasting, anomaly detection, AI-generated commentary and agentic workflows among the use cases, along with the human-in-the-loop controls that keep finance in charge of what the models produce. You'll build the data behind them, and there is every opportunity to be part of those conversations and the design work as the use cases take shape. Experience of analytics or AI work in a finance setting is a real plus, not something we expect you to arrive with. Delivery and client skills. You own your scope and follow it through, and you support the team around you. You're comfortable in workshops and working sessions with client finance and technology people, contributing to the design workstreams the lead runs in front of the client, and you produce clear analysis and options that inform the decisions taken. You bring structured, analytical problem-solving, communicate clearly under pressure, and manage expectations honestly when something is harder than it looked. Ways of working. We're increasingly an AI-first delivery team, bringing agentic tooling and orchestration into how we design, build and validate, with our people owning the judgement and the outcome. If you already work this way, that's a real plus, and we're keen to hear from people bringing that experience.