Senior Data Engineer

About Allica Bank

Allica is the UK’s fastest growing company - and the fastest-growing financial technology (Fintech) firm ever. Our purpose is to help established SMEs, one of the last major underserved opportunities in Fintech.

Established SMEs are the backbone of local communities - representing over a third of our economy - yet have been largely neglected both by traditional high street banks and modern fintech providers.

Role Description

As a Data Engineer / BI Analyst at Kriya, you'll own the flow of data from our source systems through to the reports and insights the business relies on every day. You'll build and maintain the pipelines that bring data together, model it in Power BI so teams can self-serve, and work directly with stakeholders to turn business questions into datasets and reports they can trust.

This is a broad, hands-on role spanning both data engineering and BI, with a high degree of autonomy. You'll also own key recurring data processes end-to-end and the tooling behind them (Power BI, Synapse Analytics). The role includes building Power BI semantic models and reports, but it is not primarily a business-insights or analyst position. Success means reliable pipelines, trustworthy curated data, maintainable semantic models, safe releases, and effective production support. Insights and analytics are a plus.

A key part of the role is making data accessible through AI, enabling business users to ask questions of our data in natural language, so trusted answers aren't gated behind a report request or a SQL query.

Principal Accountabilities

- Deliver trusted, analysis-ready data. Own the data flow from source systems to the reports the business relies on, so decisions are made on data people can trust.

- Keep the data platform running. Keep extraction, transformation, feeds and scheduled reports flowing reliably - "keep the lights on" for data.

- Support data-driven decisions with BI models, reports, and dashboards.

- Enable business users to self-serve through training and knowledge sharing. Make data available via AI so users can ask questions about data in natural language.

Detailed:

- Data engineering: design, implement, and optimise data pipelines for extracting, transforming, and loading (ETL/ELT) data from multiple sources, ensuring scalability, reliability, and cost-effectiveness.

- Data quality and integrity: implement best practices for data validation, cleansing, and governance to guarantee high-quality, consistent, and reliable datasets.

- Data delivery: work directly with business stakeholders to scope and build bespoke datasets and reports supporting business decisions. Maintain data dictionaries, pipelines and delivery methods (SFTP feeds, Google Sheets integrations, etc).

- Data modelling (BI): Design and maintain the semantic layer in Power BI so business users can self-serve reliably. Ensure models are well-documented and consistent.