United Kingdom · Technical roles · Mid-Level (2-5 years)

Fraud Detection Engineer

Here is the whole job, in plain words. What it is, a real day, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

Experience bandMid-Level (2-5 years)
Direct reportsNo direct reports
Reports toSenior Fraud Detection Engineer or Engineering Manager
UK framework levelUsually a coordinator, or early in a professional job

Also advertised as Mid-Level Fraud Engineer · Anti-Fraud Systems Developer · Fraud Prevention Specialist (Engineering)

Built on an analysis of 43,079 real UK job descriptions · grounded in qualifications employers recognise

Start with a free Future Fluency check, tuned to Fraud Detection Engineer

Ten quick questions, one per Future Fluency, asked against this role rather than a generic one. About five minutes, and no card.

Start the check, free

1What this role really is

This role is all about building and refining the systems that catch fraudsters in their tracks. You'll be working with real-time data, digging into patterns, and writing code that directly protects our customers and our bottom line. It's a hands-on engineering job with a direct impact on financial security, which is pretty rewarding, honestly.

2What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

Python (pandas, scikit-learn)Intermediate

Feature extraction, basic model training, data cleaning, scripting for data analysis and automation.

SQL (PostgreSQL, MySQL)Intermediate

Complex joins, data retrieval from production databases and data warehouses, ad-hoc analysis for fraud investigations.

Apache KafkaIntermediate

Consuming data from existing Kafka topics for real-time fraud detection, understanding message formats and stream processing.

Apache Spark/FlinkIntermediate

Running predefined jobs for batch processing or streaming analytics, understanding how to debug and optimise existing jobs.

Snowflake/BigQueryIntermediate

Querying data warehouses for historical analysis, feature engineering, and model training data preparation.

Databricks or KubeflowIntermediate

Training models, managing experiments, and deploying models within the existing MLOps platform.

Feast/Tecton (Feature Store)Intermediate

Retrieving features for real-time model inference, contributing new features to the store following established patterns.

Grafana and TableauIntermediate

Building and monitoring dashboards using established templates, creating custom visualisations for fraud trends, responding to alerts.

Drools, Sift, or Feedzai (Rule Engines)Intermediate

Modifying and testing existing rules, implementing new rules based on specifications, analysing rule performance.

3What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choice	Coming in	Where you are now	The step above
Feature Implementation Approach	Proposes options, requires approval from Senior Engineer.	Independently decides on approach for routine features, consults Senior Engineer for complex ones.	Makes independent technical decisions, provides guidance to junior team members.
Rule Threshold Adjustments	Suggests changes, requires approval from Risk Operations and Senior Engineer.	Independently adjusts thresholds within defined parameters, consults Risk Operations for significant changes.	Defines parameters for threshold adjustments, approves changes for junior team members.
Technical Debt Prioritisation	Identifies technical debt, escalates to Senior Engineer for prioritisation.	Prioritises and addresses technical debt within assigned features, consults manager on larger refactoring efforts.	Drives technical debt reduction initiatives across a workstream, influences roadmap.
Tool/Library Selection (within existing stack)	Suggests new libraries, requires approval.	Selects appropriate libraries/tools from approved list for specific tasks, consults on new additions.	Evaluates and recommends new tools/libraries for broader team adoption.

4How you'll be judged

The scoreboard, honestly: the hard targets, how often each one is actually looked at, and the quiet human signals that never make it onto a dashboard.

Feature Delivery Rate

The number of new fraud detection features or rule sets you successfully deploy to production.

Target · 10+ features/rule sets per quarter

In Q1, you deployed 12 new features, including a new device fingerprinting signal and three updated rules for account takeover attempts.

Model Accuracy (Assigned Models)

The precision and recall achieved by the fraud detection models or rule sets you're responsible for, against specific targets.

Target · >99% precision at a 50% recall target (for high-risk models)

Your transaction scoring model maintained 99.2% precision while catching 52% of known fraud, exceeding the target.

SLA Adherence for Rule Tuning

How quickly you respond to and resolve requests from the Risk Operations team to adjust or tune existing fraud rules.

Target · 95% of tickets resolved within a 48-hour SLA

Over the last month, you closed 28 out of 30 rule-tuning requests within two days, helping Risk Ops react quickly to new threats.

False Positive Rate (Specific Areas)

The percentage of legitimate transactions or users incorrectly flagged as fraudulent by your models or rules.

Target · Maintain or reduce false positive rate by 5% in assigned areas

You tweaked the login fraud model, reducing false positives by 7% for legitimate users while keeping detection rates stable.

Proactive Threat Identification

Your ability to independently spot emerging fraud patterns or system vulnerabilities before they become major problems.

You're often the first to flag suspicious trends in dashboards or logs. You propose new features or rule changes without being asked. You share insights from external fraud intelligence sources with the team.

Collaboration with Risk Operations

How effectively you work with the Risk Operations team to understand their challenges and translate them into technical solutions.

Risk Ops regularly comes to you for advice. You actively participate in their stand-ups or review sessions. They report that your solutions directly address their pain points and make their jobs easier.

Code Quality & Maintainability

The clarity, efficiency, and robustness of the code you write, making it easy for others to understand and extend.

Your code reviews are consistently positive, with minimal rework required. Other engineers find your contributions easy to integrate and debug. You write clear documentation for your features and models.

Problem-Solving Initiative

Your willingness to dig into complex, ambiguous problems and propose practical, effective solutions.

When faced with a tricky bug or an unclear data issue, you don't just escalate
you investigate and come with potential fixes. You're known for unblocking yourself and others on technical challenges.

5Would you like it

The honest version. What people enjoy, and what grinds them down.

What people enjoy

Solving Complex Puzzles

You love digging into messy data, finding hidden connections, and unravelling how fraudsters are trying to bypass our systems. It's like being a detective, but with code.

You spent a whole afternoon tracing a series of small, seemingly unrelated transactions back to a single, sophisticated fraud ring using graph analysis.

Direct Impact & Protecting Others

You're driven by the knowledge that your code directly prevents financial losses and protects legitimate customers from being victims. You see the immediate value of your work.

After deploying a new rule, you see a measurable drop in a specific type of fraud, knowing your work just saved thousands of pounds.

Continuous Learning & Adaptation

The fraud landscape is always changing, and that excites you. You enjoy learning about new attack vectors, new technologies, and constantly refining your skills to keep up.

You spend personal time reading security blogs and experimenting with new machine learning techniques to see how they might apply to fraud detection.

What frustrates people

The Constant Cat-and-Mouse Game: You'll spend a month building a brilliant model to stop a new fraud vector, only for fraudsters to adapt and find a new loophole within a week. The job is never 'done'.
Fighting the Product Team: Constantly being the 'no' person who has to explain why the new 'frictionless sign-up' feature is an open invitation for mass account creation by bots.
Garbage In, Garbage Out: Your models are only as good as the data you receive. Expect to spend 50% of your time cleaning up messy, incomplete, or outright wrong data from upstream services you have no control over.
The 2 AM 'All Hands on Deck': When a major, automated fraud attack is in progress, it doesn't matter if it's a weekend or a holiday. You're on the front line until the bleeding stops. Not for the faint-hearted.

What this role does not give you

A predictable, 9-to-5 routine with no urgent interruptions.
The satisfaction of seeing every single model or feature you build go live and stay live indefinitely.
A role where you only interact with other engineers and data scientists – you'll be talking to risk ops, product, and even legal quite a bit.

6Who you work with

This role directly impacts our financial security and customer trust. You're building the defences that protect millions of pounds in transactions daily. Get it right, and we save money and maintain our reputation; get it wrong, and we face significant losses and reputational damage. It's that simple, really.

Inside the business

Risk Operations Team
Product Managers (especially for new features)
Data Scientists (for model collaboration)
Backend Engineering Teams
Customer Support (for false positive feedback)

Outside the business

Payment Processors
Fraud Solution Vendors (e.g., Sift, Feedzai)

7What you need before you start

Not a wish list. The things you would be expected to already have.

At least 2 years of hands-on experience in a data-intensive engineering role, preferably with a focus on fraud, risk, or security.
Demonstrable experience writing production-quality Python code for data processing and analysis.
Solid understanding of SQL for complex data querying and manipulation.
Experience working with large datasets and distributed systems (e.g., Kafka, Spark).
A degree in Computer Science, Engineering, Mathematics, or a related quantitative field, or equivalent practical experience.

8What to practise next

Where the job is going, and what to do about it starting this week.

Advanced Streaming Data Architectures

As fraud attacks become faster and more sophisticated, our need for real-time detection at scale grows. You'll need to move beyond consuming Kafka topics to designing and building more complex streaming pipelines.

Stateful stream processing (e.g., Flink state management) · Exactly-once processing semantics · Stream-to-batch integration patterns · Performance optimisation for low-latency pipelines

This quarter: Take an online course on Apache Flink or Kafka Streams.
Next quarter: Propose and implement a small, new real-time feature using Flink or Kafka Streams.
Month 6: Participate actively in design discussions for new streaming data pipelines, offering informed opinions.
Month 9: Lead the implementation of a new, moderately complex streaming feature from design to deployment.

Quick win: Start by deeply reviewing the existing Kafka Streams or Flink codebases. Understand the current bottlenecks and propose small, immediate optimisations.

Deep Dive into Graph-Based Analysis

Fraud rings are increasingly complex, and traditional tabular data struggles to capture the intricate relationships between entities. Graph databases and algorithms are becoming essential for uncovering these hidden networks.

Graph database fundamentals (e.g., Neo4j, Amazon Neptune) · Common graph algorithms (PageRank, Community Detection) · Feature engineering from graph structures · Real-time graph traversal for inference

This quarter: Complete an online tutorial on Neo4j and Cypher.
Next quarter: Work with a senior engineer to identify a fraud pattern that could be better detected with graph analysis.
Month 6: Implement a small proof-of-concept using a graph database to detect a specific fraud ring.
Month 9: Present your findings and advocate for integrating graph features into a production model.

Quick win: Familiarise yourself with the existing graph data visualisations (if any) and try to manually trace a few known fraud cases to understand the connections.

9Staying current once you are in

What people here do to keep up

Regularly attending industry webinars or conferences on fraud, cybersecurity, or machine learning (e.g., RSA Conference, Black Hat, PyData).
Contributing to open-source projects related to fraud detection or data engineering.
Taking advanced online courses on streaming data, graph databases, or adversarial machine learning.
Participating in internal knowledge-sharing sessions and presenting on new techniques or fraud patterns you've discovered.

10How the AI economy is changing work like this

Before we ask anything of you, here's what we can already say about AI and work of this kind:

The new skill this role is being asked for: Prompt Engineering & LLM Integration

Competitors are already using Large Language Models (LLMs) to draft reports in 10 minutes that used to take 2 hours. Engineers who figure this out will outproduce peers significantly. It's not just about using ChatGPT; it's about integrating LLMs into our workflows to automate tasks and generate insights.

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

Your PlanIllustration

Built for Fraud Detection Engineer

6 units that map to this job, from the qualifications that cover it.

Help to monitor and maintain the security of the retail unitPearson Education Ltd · covers 1 of 1 standardsLevel 3
Implementing Crime Prevention Methodology in a Retail EnvironmentQualifications Network · covers 1 of 1 standardsLevel 3
Understand organisational procedures for monitoring the security and environment of items within a cultural venueQualifications Scotland · covers 1 of 1 standardsLevel 3
Reception, Billing and Cashier Procedures for Front Office StaffAIM Qualifications · covers 1 of 1 standardsLevel 3
Monitor and support secure till use during trading hoursCambridge OCR · covers 1 of 1 standardsLevel 3
Provide point of sale service within a cultural venuePearson EDI · covers 1 of 1 standardsLevel 2

These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Integration

Context windows and token limits
Temperature settings for different tasks
RAG (Retrieval Augmented Generation) architectures
Output validation and hallucination detection

What you’ll use

Skills this role draws on

Technical

Adversarial Feature Engineering
Anomaly Detection in High-Volume Streams
Graph-Based Network Analysis (Concepts)
Real-Time Model Deployment & Performance Tuning (Use)
Model Explainability for Risk & Compliance (Application)

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

1
Associate Fraud Detection Engineer (L1)
1-2 years
Skills to master
- Core Python & SQL, understanding existing fraud rules, data ingestion processes, basic model monitoring, clear documentation.
You're ready to move on when
- Consistently delivers assigned tasks on time with minimal bugs.
- Proactively asks clarifying questions and learns from feedback.
- Demonstrates a solid grasp of our core data pipelines and fraud detection tools.
- Can independently fix minor issues in existing codebases.
2
Data Analyst / Junior Data Scientist
2-3 years
Skills to master
- Strong analytical skills, data manipulation (SQL/Python), statistical analysis, basic machine learning concepts, communicating insights.
You're ready to move on when
- Has built and presented several data-driven insights projects.
- Shows a strong interest in the 'why' behind data patterns, especially suspicious ones.
- Has picked up some basic scripting skills and is eager to write more production-ready code.
- Understands the business impact of data quality and analytical accuracy.
3
Backend Software Engineer (with data interest)
2-4 years
Skills to master
- Production-grade coding, system design, API development, distributed systems, data handling at scale.
You're ready to move on when
- Has built and maintained robust backend services.
- Understands the importance of low-latency and high-availability systems.
- Has worked with data pipelines or event streaming in a previous role.
- Expresses a strong desire to apply engineering skills to complex data problems like fraud.

11Where this role leads

The long view:Your journey here is what you make it. We're committed to providing the opportunities and support for you to grow, learn, and make a significant impact. If you're excited by the challenge of outsmarting fraudsters and building robust systems, you'll find a rewarding career path with us.

Pay & demand

Pay and demand for this role will appear here, each figure traced to a named authoritative source (e.g. the ONS Annual Survey of Hours and Earnings, under the Open Government Licence). We don’t show numbers we can’t attribute.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Fraud Detection Engineer is actually changing. In about two minutes, the free confidence check asks where you stand on each of the ten. That's the whole check, and it's what makes the plan yours rather than generic.

12The team that's yours

No two people are taught the same way. This is one-to-one, not one-to-many.

Zavmo is a hyper-personalised AI learning platform. Twelve virtual tutors, each with a different way of teaching, and one orchestration agent that picks the right one for the moment. So every single lesson is shaped around you, your role, and the way you learn. Not a course everyone sits through. A conversation built for you, and no one else.

…and nine more, matched to you after your first chat. Meet all twelve

Start free and meet yours

13What it feels like

A conversation, not a course

Because your tutor knows your role, your projects and your last session, learning sounds like this. And it's different for every single person:

Help to monitor and maintain the security of the retail unitLevel 3

Applied to your work in Fraud Detection Engineer

By completing this unit, learners will be able to put security procedures into practice and monitor losses in a retail environment, demonstrating knowledge of maintaining a secure retail unit.

How the thinking builds

Remember
Understand
Apply
Analyse
Evaluate
Create

An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

One to one, not one to many

No two people run this the same way

A course is written once and handed to everyone. This is assembled around you, and keeps changing as it learns you. Five things it reads, and what each one changes.

Your actual work Every lesson is taught against a live piece of your own work, not a worked example from a textbook.
What you already know The first conversation finds your starting point, so you skip what you can already do and spend the time on what you cannot.
The conditions you learn under Not a learning-styles quiz. The evidence does not support those. The dimensions the research does back, read once and used to shape the plan.
How far you got last time It picks up mid-thought. The tutor knows what you said, what you struggled with, and what it asked you to try.
Which tutor suits the moment Twelve of them, each for a different kind of thinking. The one who walks you through a first idea is not the one who stress-tests it.

See how you learn, free. Eight questions, no sign-up. A directional taster; the diagnostic inside Zavmo goes deeper and keeps adapting.

DemonstrateIllustration

Evidenced on your work in Fraud Detection Engineer

You do not finish by watching something. You finish by showing it on the work you already do, against the measures this job is judged on.

Feature Delivery RateThe number of new fraud detection features or rule sets you successfully deploy to production.In Q1, you deployed 12 new features, including a new device fingerprinting signal and three updated rules for account takeover attempts.10+ features/rule sets per quarter
Model Accuracy (Assigned Models)The precision and recall achieved by the fraud detection models or rule sets you're responsible for, against specific targets.Your transaction scoring model maintained 99.2% precision while catching 52% of known fraud, exceeding the target.>99% precision at a 50% recall target (for high-risk models)
SLA Adherence for Rule TuningHow quickly you respond to and resolve requests from the Risk Operations team to adjust or tune existing fraud rules.Over the last month, you closed 28 out of 30 rule-tuning requests within two days, helping Risk Ops react quickly to new threats.95% of tickets resolved within a 48-hour SLA
False Positive Rate (Specific Areas)The percentage of legitimate transactions or users incorrectly flagged as fraudulent by your models or rules.You tweaked the login fraud model, reducing false positives by 7% for legitimate users while keeping detection rates stable.Maintain or reduce false positive rate by 5% in assigned areas

These are this job's own measures, with its own targets. Nothing is marked evidenced, because nobody has started this yet. Yours would fill in from the work you bring.

Your passport

This isn't a certificate you file away. It's a passport to the life you're designing.

Every credit you earn and every fluency you build adds up: evidence where it counts, carried with you. Zavmo keeps the map: where you are, where you're heading, and the next step, at your pace, around your life. From Fraud Detection Engineer to Senior Fraud Detection Engineer (L3), and whatever you decide comes after.

Level 3 · in progressAI Fluency→ Senior Fraud Detection Engineer (L3)→ your design

Where this takes you

Your journey here is what you make it. We're committed to providing the opportunities and support for you to grow, learn, and make a significant impact. If you're excited by the challenge of outsmarting fraudsters and building robust systems, you'll find a rewarding career path with us.

See Your Progress GrowIllustration

Fraud Detection Engineer

Adversarial Feature Engineering
Anomaly Detection in High-Volume Streams
Graph-Based Network Analysis (Concepts)
Real-Time Model Deployment & Performance Tuning (Use)
Model Explainability for Risk & Compliance (Application)

This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

14The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Fraud Detection Engineer is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

Senior Fraud Detection Engineer (L3)
3-5 years in current role
You'll move from owning features to owning entire systems or workstreams. You'll be making more significant technical decisions and mentoring others.
- System Design & Architecture: Designing new fraud detection services from the ground up.
- Advanced MLOps: Implementing CI/CD for ML models, managing canary deployments.
- Deep expertise in a specific fraud domain (e.g., payment fraud, account takeover).

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a big chunk of fraud detection engineering is repetitive, time-consuming work. But what if you could offload some of that to a smart assistant? We're talking about using AI to supercharge your productivity, helping you focus on the truly challenging, strategic parts of the job.

Imagine spending less time on boilerplate code or sifting through terabytes of logs, and more time actually outsmarting fraudsters. Our AI Productivity Hub isn't about replacing you; it's about giving you superpowers. Here's how it'll change your day-to-day work:

Code Automation & Debugging

Use AI to automatically generate boilerplate Python or SQL for feature extraction, test cases, or even entire data pipelines. It'll also help you debug complex issues by suggesting fixes or explaining obscure error messages, turning hours of head-scratching into minutes.

Anomaly Pattern Suggestion

An AI assistant continuously scans terabytes of log data and transaction streams, identifying novel and subtle correlations that often precede known fraud events. It proactively suggests new, non-obvious behavioural patterns for you to investigate, giving you a massive head start on emerging threats.

Adversarial Attack Simulation

Leverage generative AI to create synthetic data that mimics emerging fraudster techniques. This allows you to stress-test your models against attacks you haven't even seen in the wild yet, moving from a reactive to a truly proactive defence posture. It's like having a fraudster simulator on demand.

Model Explainability & Documentation

Automatically generate model cards and human-readable reports from complex SHAP/LIME outputs. This translates intricate model decisions into clear business language for risk, compliance, and legal teams, drastically cutting down on manual report writing and making your work much easier to communicate.

Common questions

How do you become a Fraud Detection Engineer?

Common routes in include Associate Fraud Detection Engineer (L1) (1-2 years), Data Analyst / Junior Data Scientist (2-3 years) and Backend Software Engineer (with data interest) (2-4 years). Times vary with prior experience.

Where can a Fraud Detection Engineer progress to?

This role can lead on to Senior Fraud Detection Engineer (L3) (3-5 years in current role), depending on the skills you build.

What level is a Fraud Detection Engineer in the UK?

This role aligns to RQF Level 3 on the UK framework, a guide to the depth of qualification it maps to, not a hard entry bar.

What new skills matter most for a Fraud Detection Engineer?

Increasingly, Prompt Engineering & LLM Integration. These are the areas where the higher-paid, future-proof work is heading.

The honest bit

You’ve started things before

Most of them were built for a room full of people who aren’t you. A cohort moves on whether or not your week allowed it, and by the third week the thing you’re behind on becomes the reason you stop opening it.

There’s no cohort here, and no timetable to fall behind. Before anything starts, Zavmo asks when you’re sharpest and how long you can realistically sit down for, then builds the sessions around those answers. A bad fortnight changes your pace. It doesn’t put you behind.

And you only pay once you start learning. Searching and planning are free, and you can cancel any time — so the cost of finding out is an afternoon, not a year.

What it costs

Less than one coaching session. Every month.

A single career-coaching hour costs more than a month of this, and it ends when the hour does. Zavmo doesn't. It's £70 a month, about £2.30 a day, for a companion that knows a Fraud Detection Engineer, works on the job you actually do, and keeps going at your pace rather than a timetable's.

Searching and planning stay free. You only pay when you start learning.
Your credits are yours. Regulated, and they don't vanish when a subscription ends.
Cancel any time and billing stops. No notice period, no minimum term.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Build my plan, free Here's the first ten minutes: a 2-minute confidence check → your personalised roadmap → meet the tutors matched to you. No card, cancel any time. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.

Fraud Detection Engineer

What's rising in its place

Prompt Engineering & LLM Integration

Skills this role draws on

Technical

How you actually get there, here

Associate Fraud Detection Engineer (L1)

Data Analyst / Junior Data Scientist

Backend Software Engineer (with data interest)

The credential is what you can do today. These are what keep you valuable.

Roles this leads to

No two people are taught the same way. This is one-to-one, not one-to-many.

A conversation, not a course

No two people run this the same way

This isn't a certificate you file away. It's a passport to the life you're designing.

Your journey here is what you make it. We're committed to providing the opportunities and support for you to grow, learn, and make a significant impact. If you're excited by the challenge of outsmarting fraudsters and building robust systems, you'll find a rewarding career path with us.

Everything else the record holds

The career path, and where it branches

Senior Fraud Detection Engineer (L3)

Where AI is starting to help

Code Automation & Debugging

Anomaly Pattern Suggestion

Adversarial Attack Simulation

Model Explainability & Documentation

Common questions

You’ve started things before

Less than one coaching session. Every month.

You have the map. Walking it is the part we do together.