Lead Analytics Engineer F - M - X H/F

In April 2026, Otis, the world’s largest elevator and escalator company, became our majority shareholder. We keep running as we always have: same team, same brand, same mission. What changes is the scale of what we can go after, and the means behind us to do it.

Why join us?

Because here, you don’t have to choose between impact and stability.

Large groups tend to offer stability with little room to manoeuvre. Smaller organisations offer more freedom, sometimes without clear direction. We aim to sit between the two:

• Autonomy, with the tools and the support to use it
• A culture built on trust and transparency
• Opportunities for growth and internal mobility
• A team that values commitment and follow-through

And our values guide us every day:

• Care: Working with respect and kindness, internally as well as with our customers
• Grit: Seeing things through, with high standards and determination
• Uniqueness: Daring to be yourself, sharing your ideas and challenging the status quo

Who we’re looking for

We are looking for a Lead Analytics Engineer to be the right hand of our CTO on data, and the right hand of our Global Head of Operations on making a 350-person, four-country operation data-driven.

It is one job made of two halves. If what you want is to go deep on modelling and be left alone, this is not the role. Roughly half of it is sitting with Operations, in the UK, France, Singapore and Hong Kong, arguing about what we should be measuring and why, and then being the person who makes that measurable.

We’re not starting from scratch. There’s a data lake, dbt on BigQuery, Looker on top, and it works. Software engineers, Data scientist and even some business champions commit to it daily. Nobody owns all of it though, which is where you come in.

This is a hands-on individual contributor role with no direct reports. You will not manage a team, and we are not planning to build one under you. You will set the standard for how data lands in our lake and how it gets used, and hold the rest of the engineering team to it.

You’ll join a Product & Engineering team of around 20 people, growing by 50% over the next year.

Your day-to-day at WeMaintain

As Lead Analytics Engineer, you will:

• Bring new data into the lake, constantly. This is the steady heartbeat of the role: new sources, new fields from sources we already have, new needs from every part of the business. None of it is rocket science, but our business is a dense one. Four countries, thousands of buildings, an IoT fleet, field engineers, contracts, regulation. Modeling it well is the hard part.
• Own the modeling layer. dbt on BigQuery today: models, tests, documentation, reliability. You decide how it evolves, including whether dbt is still the right answer in two years, or when the IoT fleet has grown tenfold.
• Define what “good” looks like for our operational KPIs. Our field and operations teams know their business better than anyone. What they don’t have is someone technical standing next to them, turning that knowledge into numbers everyone trusts. You’ll be that person, with Marine, our Global Head of Operations, as your closest counterpart: deciding what we count, how we count it, and what a result has to show before it means we’re winning.
• Help one standard become true in four countries. We operate in the UK, France, Singapore and Hong Kong, and good practice invented in one of them should become how the whole group works. Spotting it and rolling it out is Operations’ call. Making sure it’s made on evidence rather than instinct, and that we can tell afterwards whether it worked, is yours.
• Build the analysis the business doesn’t have yet: client churn prediction, contract-level economics, engineer productivity, and what our IoT devices tell us about how lifts are actually used.
• Keep the platform fast and affordable. BigQuery cost control is your call, not an afterthought.
• Extend the platform where it needs extending: UDFs, signed links, LLMs running directly in the warehouse, and whatever else the next problem needs.
• Raise the standard around you. Engineers across the team push data into the lake. You set the rules of the road, and you push back when they’re not followed.

On AI, specifically

Use them. Obviously. We’d think it strange if you didn’t.

The tooling has moved the goalposts in the last two years, for how we ship and for what we can ship at all. We’d rather talk about what that makes possible than about which model we’re on this month. It doesn’t remove the need for strong engineers. It makes them more valuable, because someone still has to know what good looks like. Use it however you see fit. You won’t be BS’d or FOMO’d about it here.

And a word on the field

From the outside the industry looks remote and a bit dull. It’s neither, and what you find once you’re in it is how tightly coupled everything has to be. Problem detection feeds the device state service, which needs the SLA on the contract, which depends on the customer, and which regulation reads differently in each of our four countries. Very little here is a self-contained feature.

The data you will provide will change how someone’s day goes on site. You’ll see that for yourself during onboarding. It isn’t a day-to-day part of the job, but it’s the reason the job matters.

What we’re looking for

You might be a great fit if you:

• Have been the person who owned a data platform end to end, from ingestion through to the numbers a leadership team argues over. Ideally without a large team behind you.
• Have done both halves. You’ve written the pipeline and you’ve sat in the room where the business decided what to do about the result. Neither one feels like someone else’s job to you.
• Are fluent in SQL, data lakes, transformation and ETL. We care much less about which tools you learned them in.
• Are comfortable being the standard-bearer without the authority. Influence and credibility, not a reporting line.
• Can tell an operator they’re measuring the wrong thing and be listened to, because you understood their problem first.
• Bring structure to messy operational data and are not upset when reality refuses to be clean.
• Are at ease working fully remotely across four countries and two continents, and take the initiative to stay close to people.

We do not require experience with our specific stack. If you’ve never touched dbt, BigQuery or Looker but you’ve built and owned the equivalent elsewhere, we want to talk to you.

English is the working language of this role.

Bonus points if

• You’ve started, run or helped build something of your own, and you judge your work by what it changed rather than the code you wrote.
• You’ve worked in an operations-heavy business: field service, logistics, industrial, energy, anywhere the data describes physical things happening in the world.
• You’ve worked with IoT or time-series data.
• You have hands-on experience with dbt, BigQuery or Looker.
• You’ve introduced AI tooling into a data team in a way that stuck.
• You’ve worked across multiple countries and know how differently the same process can look in each one.

We don’t expect you to have seen everything already. We do expect you to ramp fast and take ownership quickly.

Why this role is different

You’d own the data layer of a company that is currently working out how it wants to run globally. Most of the big questions haven’t been answered yet, and you’d be in the room for them.

Senior data people usually end up either close to the warehouse or close to the business. Here it’s both, with a direct line to the CTO and to the Global Head of Operations.

And it’s full remote, properly. The whole team gets together in person three times a year.

Have done both halves. You’ve written the pipeline and you’ve sat in the room where the business decided what to do about the result. Neither one feels like someone else’s job to you.

Are fluent in SQL, data lakes, transformation and ETL. We care much less about which tools you learned them in.

Are comfortable being the standard-bearer without the authority. Influence and credibility, not a reporting line.

Can tell an operator they’re measuring the wrong thing and be listened to, because you understood their problem first.

Bring structure to messy operational data and are not upset when reality refuses to be clean.

Are at ease working fully remotely across four countries and two continents, and take the initiative to stay close to people.

We do not require experience with our specific stack. If you’ve never touched dbt, BigQuery or Looker but you’ve built and owned the equivalent elsewhere, we want to talk to you.

English is the working language of this role.

• You’ve started, run or helped build something of your own, and you judge your work by what it changed rather than the code you wrote.
• You’ve worked in an operations-heavy business: field service, logistics, industrial, energy, anywhere the data describes physical things happening in the world.
• You’ve worked with IoT or time-series data.
• You have hands-on experience with dbt, BigQuery or Looker.
• You’ve introduced AI tooling into a data team in a way that stuck.
• You’ve worked across multiple countries and know how differently the same process can look in each one.

You’ve started, run or helped build something of your own, and you judge your work by what it changed rather than the code you wrote.

You’ve worked in an operations-heavy business: field service, logistics, industrial, energy, anywhere the data describes physical things happening in the world.

You’ve worked with IoT or time-series data.

You have hands-on experience with dbt, BigQuery or Looker.

You’ve introduced AI tooling into a data team in a way that stuck., postgraduate degree EducationalOccupationalCredential bachelor degree EducationalOccupationalCredential associate degree

An interview with a member of our team, like (Principal Engineer) 60 mins A working conversation about how you’ve built and owned data platforms, the calls you’ve made, and what you’d want to own here. Make sure the role is a good fit.

• A practical exercise, followed by a 90-minute debrief with (CTO) and another senior engineer We send you a problem a few days ahead. Spend 30 minutes on it. No code, no slides, no prep deck. Then we talk it through together. We’re interested in how you think, what you ask, and where you push back. The artefact doesn’t matter.
• A final round: 45 mins with (Global Head of Operations), then 45 mins with 2-3 members of the team With Marine, a conversation about the business half of the role: how you work with operators, and how you’d approach defining what good looks like. With the team, collaboration style, ownership, and expectations on both sides.

Welcome to the team!

Salary

As this is a Europe-wide role, compensation is adapted to the candidate’s country of location and aligned with local market benchmarks.

€80,000 - €105,000 base salary, depending on your country., You’ve worked across multiple countries and know how differently the same process can look in each one.

We don’t expect you to have seen everything already. We do expect you to ramp fast and take ownership quickly.

You’d own the data layer of a company that is currently working out how it wants to run globally. Most of the big questions haven’t been answered yet, and you’d be in the room for them.

Senior data people usually end up either close to the warehouse or close to the business. Here it’s both, with a direct line to the CTO and to the Global Head of Operations.

And it’s full remote, properly. The whole team gets together in person three times a year.

Every application is reviewed carefully. Tell us who you are, what attracts you to this role, and what you’d like to build with us.

We value motivation, curiosity and potential over a “perfect” background.

To apply, click the “Apply” button below and answer a few questions in the form. It’s quick, and it helps us get to know you better.

At WeMaintain, we value diversity, encourage personal development, and are committed to inclusion. Whatever your background, story or identity, you are welcome to apply.

Four stages. We move quickly and we’ll keep you posted at every step.

An interview with a member of our team, like (Principal Engineer) 60 mins A working conversation about how you’ve built and owned data platforms, the calls you’ve made, and what you’d want to own here. Make sure the role is a good fit.

A practical exercise, followed by a 90-minute debrief with (CTO) and another senior engineer We send you a problem a few days ahead. Spend 30 minutes on it. No code, no slides, no prep deck. Then we talk it through together. We’re interested in how you think, what you ask, and where you push back. The artefact doesn’t matter.

A final round: 45 mins with (Global Head of Operations), then 45 mins with 2-3 members of the team With Marine, a conversation about the business half of the role: how you work with operators, and how you’d approach defining what good looks like. With the team, collaboration style, ownership, and expectations on both sides.

Welcome to the team!

As this is a Europe-wide role, compensation is adapted to the candidate’s country of location and aligned with local market benchmarks.

€80,000 - €105,000 base salary, depending on your country.

The exact package will depend on experience, level of ownership, and demonstrated impact. We’ll confirm the specific range for your country before your first interview.

False

In 2017, after more than ten years in the lift industry across Europe and Asia, noticed a system stuck in its ways, where speed takes priority over quality and frontline teams remain invisible.

With , he founded WeMaintain to put technology and trust back into building maintenance, and the people doing the work back at the centre of it. Their ambition: give technicians back their autonomy, build the IoT technology in-house, and design the service around the people who use it.

Based in Paris, London, Singapore and Hong Kong, we maintain lifts, escalators, automatic doors and fire safety systems. With 350+ employees, we combine on-the-ground expertise with data-driven management to deliver maintenance that performs and that our clients can see into., The exact package will depend on experience, level of ownership, and demonstrated impact. We’ll confirm the specific range for your country before your first interview.

Full remote · Europe · Product & Engineering

In 2017, after more than ten years in the lift industry across Europe and Asia, noticed a system stuck in its ways, where speed takes priority over quality and frontline teams remain invisible.

With , he founded WeMaintain to put technology and trust back into building maintenance, and the people doing the work back at the centre of it. Their ambition: give technicians back their autonomy, build the IoT technology in-house, and design the service around the people who use it.

Based in Paris, London, Singapore and Hong Kong, we maintain lifts, escalators, automatic doors and fire safety systems. With 350+ employees, we combine on-the-ground expertise with data-driven management to deliver maintenance that performs and that our clients can see into.

In April 2026, Otis, the world’s largest elevator and escalator company, became our majority shareholder. We keep running as we always have: same team, same brand, same mission. What changes is the scale of what we can go after, and the means behind us to do it.

Because here, you don’t have to choose between impact and stability.

Large groups tend to offer stability with little room to manoeuvre. Smaller organisations offer more freedom, sometimes without clear direction. We aim to sit between the two:

• Autonomy, with the tools and the support to use it
• A culture built on trust and transparency
• Opportunities for growth and internal mobility
• A team that values commitment and follow-through

Autonomy, with the tools and the support to use it

A culture built on trust and transparency

Opportunities for growth and internal mobility

A team that values commitment and follow-through

And our values guide us every day:

• Care: Working with respect and kindness, internally as well as with our customers
• Grit: Seeing things through, with high standards and determination
• Uniqueness: Daring to be yourself, sharing your ideas and challenging the status quo

Care: Working with respect and kindness, internally as well as with our customers

Grit: Seeing things through, with high standards and determination

Uniqueness: Daring to be yourself, sharing your ideas and challenging the status quo

We are looking for a Lead Analytics Engineer to be the right hand of our CTO on data, and the right hand of our Global Head of Operations on making a 350-person, four-country operation data-driven.

It is one job made of two halves. If what you want is to go deep on modelling and be left alone, this is not the role. Roughly half of it is sitting with Operations, in the UK, France, Singapore and Hong Kong, arguing about what we should be measuring and why, and then being the person who makes that measurable.

We’re not starting from scratch. There’s a data lake, dbt on BigQuery, Looker on top, and it works. Software engineers, Data scientist and even some business champions commit to it daily. Nobody owns all of it though, which is where you come in.

This is a hands-on individual contributor role with no direct reports. You will not manage a team, and we are not planning to build one under you. You will set the standard for how data lands in our lake and how it gets used, and hold the rest of the engineering team to it.

You’ll join a Product & Engineering team of around 20 people, growing by 50% over the next year.

As Lead Analytics Engineer, you will:

• Bring new data into the lake, constantly. This is the steady heartbeat of the role: new sources, new fields from sources we already have, new needs from every part of the business. None of it is rocket science, but our business is a dense one. Four countries, thousands of buildings, an IoT fleet, field engineers, contracts, regulation. Modeling it well is the hard part.
• Own the modeling layer. dbt on BigQuery today: models, tests, documentation, reliability. You decide how it evolves, including whether dbt is still the right answer in two years, or when the IoT fleet has grown tenfold.
• Define what “good” looks like for our operational KPIs. Our field and operations teams know their business better than anyone. What they don’t have is someone technical standing next to them, turning that knowledge into numbers everyone trusts. You’ll be that person, with Marine, our Global Head of Operations, as your closest counterpart: deciding what we count, how we count it, and what a result has to show before it means we’re winning.
• Help one standard become true in four countries. We operate in the UK, France, Singapore and Hong Kong, and good practice invented in one of them should become how the whole group works. Spotting it and rolling it out is Operations’ call. Making sure it’s made on evidence rather than instinct, and that we can tell afterwards whether it worked, is yours.
• Build the analysis the business doesn’t have yet: client churn prediction, contract-level economics, engineer productivity, and what our IoT devices tell us about how lifts are actually used.
• Keep the platform fast and affordable. BigQuery cost control is your call, not an afterthought.
• Extend the platform where it needs extending: UDFs, signed links, LLMs running directly in the warehouse, and whatever else the next problem needs.
• Raise the standard around you. Engineers across the team push data into the lake. You set the rules of the road, and you push back when they’re not followed.

Bring new data into the lake, constantly. This is the steady heartbeat of the role: new sources, new fields from sources we already have, new needs from every part of the business. None of it is rocket science, but our business is a dense one. Four countries, thousands of buildings, an IoT fleet, field engineers, contracts, regulation. Modeling it well is the hard part.

Own the modeling layer. dbt on BigQuery today: models, tests, documentation, reliability. You decide how it evolves, including whether dbt is still the right answer in two years, or when the IoT fleet has grown tenfold.

Define what “good” looks like for our operational KPIs. Our field and operations teams know their business better than anyone. What they don’t have is someone technical standing next to them, turning that knowledge into numbers everyone trusts. You’ll be that person, with Marine, our Global Head of Operations, as your closest counterpart: deciding what we count, how we count it, and what a result has to show before it means we’re winning.

Help one standard become true in four countries. We operate in the UK, France, Singapore and Hong Kong, and good practice invented in one of them should become how the whole group works. Spotting it and rolling it out is Operations’ call. Making sure it’s made on evidence rather than instinct, and that we can tell afterwards whether it worked, is yours.

Build the analysis the business doesn’t have yet: client churn prediction, contract-level economics, engineer productivity, and what our IoT devices tell us about how lifts are actually used.

Keep the platform fast and affordable. BigQuery cost control is your call, not an afterthought.

Extend the platform where it needs extending: UDFs, signed links, LLMs running directly in the warehouse, and whatever else the next problem needs.

Raise the standard around you. Engineers across the team push data into the lake. You set the rules of the road, and you push back when they’re not followed.

Use them. Obviously. We’d think it strange if you didn’t.

The tooling has moved the goalposts in the last two years, for how we ship and for what we can ship at all. We’d rather talk about what that makes possible than about which model we’re on this month. It doesn’t remove the need for strong engineers. It makes them more valuable, because someone still has to know what good looks like. Use it however you see fit. You won’t be BS’d or FOMO’d about it here.

From the outside the industry looks remote and a bit dull. It’s neither, and what you find once you’re in it is how tightly coupled everything has to be. Problem detection feeds the device state service, which needs the SLA on the contract, which depends on the customer, and which regulation reads differently in each of our four countries. Very little here is a self-contained feature.

The data you will provide will change how someone’s day goes on site. You’ll see that for yourself during onboarding. It isn’t a day-to-day part of the job, but it’s the reason the job matters.

You might be a great fit if you:

• Have been the person who owned a data platform end to end, from ingestion through to the numbers a leadership team argues over. Ideally without a large team behind you.
• Have done both halves. You’ve written the pipeline and you’ve sat in the room where the business decided what to do about the result. Neither one feels like someone else’s job to you.
• Are fluent in SQL, data lakes, transformation and ETL. We care much less about which tools you learned them in.
• Are comfortable being the standard-bearer without the authority. Influence and credibility, not a reporting line.
• Can tell an operator they’re measuring the wrong thing and be listened to, because you understood their problem first.
• Bring structure to messy operational data and are not upset when reality refuses to be clean.
• Are at ease working fully remotely across four countries and two continents, and take the initiative to stay close to people.

Have been the person who owned a data platform end to end, from ingestion through to the numbers a leadership team argues over. Ideally without a large team behind you.