Senior Analytics Engineer

FYLD; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

FYLD is seeking a senior analytics lead who will own the analytics stack, from data model to self-serve insights. You will work in Engineering alongside Customer Success and Sales to drive trusted data and actionable analytics for executives and frontline teams.

The role combines building the data layer, validating data quality, and performing deep-dive analysis to answer high-stakes business questions. Remote UK with occasional travel to London is supported.

Qualifications

• 5+ years in data or product analytics with insights that changed decisions
• Strong SQL and dbt usage in production
• Experience building dashboards that users actually rely on
• Fluency with SaaS metrics like adoption, engagement, retention

Responsibilities

• Extend and optimise BigQuery datasets powering dashboards and AI-driven self-serve
• Automate repetitive reporting and data processes
• Help colleagues derive answers from self-serve tools and know when to intervene
• Collaborate with Engineering/DevOps to improve data infrastructure performance and scalability
• Partner with Product, CS, and Sales on event tracking and data collection
• Measure adoption, engagement and retention across personas and features
• Define success metrics for releases and report outcomes accurately
• Quantify value delivered and provide ROI signals to Sales and customers

Skills

SQL

dbt

SaaS metrics

Analytics storytelling

Data visualization

Self-starter

AI native

User focus

Tools

BigQuery

Power BI

Mixpanel

GitHub

Python

Excel

AI tools

Job description

Every day, thousands of field workers run jobs, complete risk assessments and capture evidence on FYLD. That data shows how our product is used, where our AI is spotting hazards, and how safety and productivity change for our customers. We’re looking for someone to own the analytics stack and drive it forwards.

This is FYLD’s dedicated analytics role. You’ll sit in Engineering and work day to day with Customer Success and Sales. Much of FYLD now answers routine data questions itself, using AI tools connected directly to our analytics layer. This role has two halves: build and maintain the trusted data layer that everyone relies on, and take on the questions self-serve can’t answer. Those are usually the messy, specific, high-stakes ones.

You’ll be hands-on across the whole stack, from dbt models to the story you tell a customer’s operations director.

What you’ll own

• The analytics data model — dbt models in BigQuery that are clean, tested, documented and governed, including the datasets our AI tools query
• Metric definitions — one consistent definition of adoption, engagement, retention and value, used by Product, CS, Sales and leadership alike
• Data quality — testing, validation and monitoring, so people and AI tools get numbers they can trust
• Product instrumentation — working with Product and Engineering on event tracking and data collection that answers the questions we’ll ask next
• Deep-dive analysis — the questions that self-serve and dashboards can’t answer, from first framing to recommendation

What you’ll do

In the Engineering team

• Extend and optimise the BigQuery datasets that power Power BI dashboards and AI-driven self-serve
• Automate repetitive reporting and data processes
• Help colleagues get better answers from self-serve tools, and know when a question needs you
• Work with the wider Engineering and DevOps community to improve the performance and scalability of our data infrastructure

With Product

• Measure adoption, engagement and retention across personas, features and customers
• Define success metrics for releases, and report honestly on whether they moved
• Partner with Product and Engineering on event tracking in Mixpanel and wider data collection

With Customer Success

• Build analysis for specific customers, such as depot-level usage, safety trends and evidence for business reviews
• Give CSMs early signals on account health and churn risk
• Help CS and Support size the impact of bugs and incidents on real users

With Sales

• Quantify what FYLD delivers — for example, how often our AI identifies hazards or how much time crews save — so Sales can build credible ROI cases
• Turn anonymised, aggregated platform data into benchmarks prospects can trust

What we’re looking for

• Strong SQL and dbt. You’ve built, tested and maintained analytical data models in production, ideally in BigQuery or another cloud warehouse, using a Git-based workflow
• SaaS metrics fluency. You understand adoption, engagement and retention, and know how to measure them across users, features and accounts
• A proven analytics track record. 5+ years in data or product analytics, with examples of insight that changed a product or business decision
• Data visualisation. Strong experience building dashboards people actually use, ideally in Power BI
• Storytelling with data. You can explain findings clearly to non-technical audiences, from a CSM to a customer’s operations director
• User focus. You understand the problem before building anything. You deliver what solves it, not just the dashboard or dataset you were asked for
• Self-starter. You’ll be our only dedicated analyst. You can prioritise across Product, CS and Sales requests and explain the trade-offs
• AI native. You use modern AI tools to work faster, and you’re curious about how AI changes the way a business uses its data

Nice to have: Python for deeper statistical or data-quality work. Mixpanel or similar product analytics tools. Experience structuring data for AI or LLM use, such as semantic layers and well-documented models. Start-up or scale-up experience. Exposure to field operations, utilities or infrastructure data.

We know not everyone will meet every requirement. If you’re close, or believe your experience is relevant,

Tools you’ll use

• SQL and dbt — data modelling and transformation
• BigQuery — our cloud data warehouse
• Power BI and Mixpanel — dashboards and product analytics
• GitHub — version control for models and analysis
• Python and Excel — deeper analysis, data-quality checks and quick modelling
• AI tools — including AI assistants connected directly to our data

Location

• Remote UK, with occasional travel to our London office
• Occasional visits to customers and field sites to see the work behind the data