Analytics Engineer

Onmo; City Of London; On-site; GBP 60,000 - 80,000; Full time

Job summary

Onmo is seeking an Analytics Engineer to own the gold-layer models, governed metrics, and self-serve tools used by Finance, Credit Risk, Collections, and Operations. You will translate credit policy and finance rules into trusted numbers, sitting between data engineering and business teams to deliver explainable analytics.

You will build production-ready data models, define metric definitions, and ensure data quality, with CI/CD and deployment workflows to keep dashboards and Genie agents

Qualifications

• 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.

Responsibilities

• Build and extend our gold-layer dimensional models, including point-in-time and month-end facts.
• Define and maintain governed metric definitions for credit and finance KPIs so every dashboard 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.

Skills

SQL

Python

Dimensional modelling

Semantic layer

CI/CD

Databricks

Git

Data quality

Workflow orchestration

Communication

Tools

Databricks Asset Bundles

Lakeflow Spark Declarative Pipelines

Terraform

Git

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.

RESPONSIBILTIES

• 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.