Analytics Engineer

ONMO; Greater London; On-site; GBP 70,000 - 100,000; Full time

Job summary

ONMO is seeking an Analytics Engineer to own the business-ready layer of our Databricks lakehouse, translating policy into trusted numbers used by Finance, Credit Risk and Operations. You will create dimensional models, define governed metrics, and build self-serve tools for dashboards and Genie agents.

You will bridge data engineering and business, automate checks, and oversee CI/CD, environment promotion, and data quality. A growth-focused team awaits those who collaborate well.

Qualifications

• 2–4 years analytics engineering experience in production data models.
• Background in consumer credit, lending or banking terminology.
• Strong SQL and Python experience.
• Hands-on dimensional modelling and a semantic/metrics layer.
• Experience with a modern cloud data platform (Databricks).
• Familiar with version control and CI/CD tooling.
• Clear communicator across finance and engineering.

Responsibilities

• Build and extend gold-layer dimensional models and facts.
• Define and maintain governed metrics for credit/finance KPIs.
• Translate policy into tested SQL and reconcile with GL.
• Build Genie agents over certified datasets.
• Add data quality checks, reconciliations and alerts.
• Deploy and operate pipelines with CI/CD and Databricks assets.

Skills

SQL

Python

Dimensional modelling

Metrics layer

Databricks

Git

CI/CD

Communication

Tools

Databricks

Terraform

Lakeflow

Job description

About The Role

As an Analytics Engineer at Onmo, you will be the day-to-day owner of the business-ready layer of our Databricks lakehouse: the dimensional models, governed metric definitions and self-serve tools that Finance, Credit Risk, Collections and Operations rely on. You will sit between data engineering and the business, turning credit policy and finance rules into trusted, explainable numbers.

RESPONSIBILITIES

• Build and extend our gold-layer dimensional models, including point-in-time and month-end facts.
• Work with the Data team and business owners to define and maintain governed metric definitions for credit and finance KPIs (arrears and DPD buckets, roll rates, charge-off, utilisation, interest and fee income) so every dashboard and Genie agent uses the same numbers.
• Translate credit policy into tested SQL and reconcile it against the general ledger and legacy reports.
• Build Genie agents over certified, documented datasets.
• Put data quality checks, reconciliations and alerts around everything you ship.
• Deploy and operate what you build: job and workflow orchestration, environment promotion and CI/CD through Databricks Asset Bundles.

FCA Compliance & Consumer Duty

• At Onmo we all take collective responsibility for our individual roles in creating the best outcomes for our customers. In this role that includes;
• Following the FCA Conduct Rules;
• You must act with integrity
• You must act with due skill, care and diligence
• You must be open and cooperative with the FCA, PRA and other regulators
• You must pay due regard to the interests of customers and treat them fairly
• You must observe proper standards of market conduct

ABOUT YOU

Ways of Working

• Collaborative in a fast-paced environment, comfortable bridging engineers and finance or credit teams.
• Automate the repeatable, document as you go, and put checks and balances around every number that leaves the platform.
• Treat metric definitions as code: versioned, reviewed and tested.

Your Approach

• Curious about how a credit card works end to end and how it shows up in the ledger.
• Comfortable with ambiguity, and enjoy getting stakeholders to one agreed definition.
• Happy at a growing company where everyone rolls up their sleeves.

QUALIFICATIONS & EXPERIENCE

Essential

• 2–4 years of analytics engineering experience, including building and maintaining data models in production.
• Background in consumer credit, lending, cards or banking, including delinquency, roll rates, charge-off, forbearance or ledger reconciliation.
• Strong SQL and working Python.
• Hands‑on dimensional modelling and experience defining a semantic or metrics layer.
• Experience with a modern cloud data platform (Databricks preferred), git and CI.
• Comfortable deploying and scheduling your own work: workflow orchestration and code‑based deployment (Databricks Asset Bundles or similar).
• Clear communicator across finance and engineering audiences.

Desirable

• Experience with loan management system, credit bureau or CRM data.
• Databricks Asset Bundles, Lakeflow Spark Declarative Pipelines, Terraform.