Metadata developer (Python)

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Lightspeed's Data Office is hiring a Senior Analytics Engineer to design and own the data models that drive decisions across financial performance, GTM activities, and product insights. You'll partner with stakeholders across Finance, Revenue Ops, Marketing, and Product to deliver the analytics foundation the business runs on.

If you are someone who enjoys building data pipelines that power real business decisions and want to design canonical models that unify fragmented source systems across multiple CRMs, ERPs, and product platforms, then this role is for you. You can sit down with Finance to talk revenue recognition or with Product to map activation funnels, and walk away with a clear data model in your head. You hold a high bar on quality, push back constructively when it matters, and don't wait to be handed a well-defined problem. You scope the work, drive it to completion, and bring others along.

What you’ll be responsible for:

• Design and build the data models that power the business: financial reporting (GTV, MRR, revenue recognition), GTM analytics, and product and operational insights

• Design for scale and maintainability while staying pragmatic, shipping incremental value, gathering feedback, and iterating as you go

• Turn ambiguous business requirements from Finance, Revenue Ops, Marketing, and Product teams into well-scoped technical solutions with effort estimates

• Collaborate across our Hub & Spoke architecture, partnering with upstream data owner teams, downstream Looker/LookML developers, and other domain pods (Retail, Hospo, Operations) to design models that work end-to-end

• Treat documentation as a first-class deliverable: column descriptions, model contracts, and architectural notes that power both human reviewers and AI/LLM-assisted workflows

• Diagnose root problems, not symptoms, and drive systemic fixes rather than one-off patches

• Own pipeline health end-to-end: monitor proactively, lead incident response, and surface risks before they become delivery problems

• Autonomously manage personal sprint commitments and contribute to team-level planning, including task breakdown, effort estimation, and priority sequencing

• Mentor junior engineers through coaching, code reviews, and technical sessions, and set the bar for what good looks like

What you will bring to the team:

• Expert-level SQL: query performance, clean, maintainable code, and advanced analytical techniques (e.g. window functions)

• Proven experience designing enterprise-grade dimensional models on modern cloud data warehouses such as BigQuery or Snowflake, including partitioning, incremental patterns, and performance optimization

• Hands-on experience modelling SaaS / CRM / ERP source systems such as Salesforce or NetSuite, with a clear understanding of how their object models translate into clean dimensional schemas

• Proven Dataform or dbt expertise in production: materializations, incremental strategies, snapshots, and macros

• You communicate effectively with technical and non-technical stakeholders, clarifying needs, surfacing tradeoffs, and driving alignment

• A security and compliance mindset: GDPR, SOX, and data access controls are part of the job, not an afterthought

Some additional things that would be a bonus:

• CI/CD experience (e.g. GitHub Actions): automated testing, linting, and deployment workflows

• Python or JavaScript for data engineering tooling

• LookML or equivalent semantic layer experience

• Familiarity with data contracts for pipeline validation

• Experience using AI/LLM-assisted tooling to accelerate development workflows: code generation, documentation, debugging, or review

• Revenue/finance domain knowledge (ARR, MRR, NetSuite, revenue arrangements)

What’s in it for you:

• Real ownership: you'll own canonical data models that leadership relies on, with the autonomy to design them properly and the influence to evolve team standards

• A modern, well-maintained stack (Dataform, BigQuery, Looker, GitHub Actions, OpenCode) with active investment in developer experience and tooling

• High-quality peers who care about craft, code reviews that make you better, and time to do things the right way

• Direct line of sight to business impact: your work powers conversations with the CFO, GTM leadership, and Product teams

• A growing Data Office at a publicly traded company headquartered in Montreal

Be a changemaker, You’ll enjoy:

• A flexible work environment that empowers you to do your best work

• A culture that celebrates performance

• The chance to make an impact in a team that’s big enough for career growth, but lean enough to make your voice heard

• Career-defining opportunities

Plus benefits designed to keep you happy, healthy and fulfilled.

• Flexible paid time off and remote work policies

• Equity options, because this is your company too

• Contributions to your pension plan. Your future matters

• Training opportunities to grow your skills and career

• Health and wellness credit so you feel your best

• Time off to volunteer and give back to your community

• Interest groups, employee-led networks, social committees to sponsored sports teams

• Computer purchase program to get your personal MacBook

• Enhanced parental leave to support growing families

• Lightspeed equity scheme (we are all owners)

• Unlimited paid time off policy

• People Experience - Our PX benefit allows you to work from pretty much anywhere in the world for 60 days a year

• Health insurance: Immediate access to health, dental, vision, and life insurance for you and your eligible dependents.

• Employer RRSP contribution of up to 4% of base salary to a maximum of $5,000

• Health and wellness benefit of $500 per year

• Employee Technology Purchase Program: Ability to an interest-free loan to purchase certain Apple products

• Paid leave and assistance for new parents

• Mental health online platform and counselling & coaching services

• Volunteer day

• We got you covered! A variety of snacks, coffee, and teas are always accessible and available in the office kitchen - Tuesday: Pancakes, Wednesday: Lunch is served, and Thursday is Happy Hour with a variety of drinks and snacks.

Fuel your growth. Find your people.

At Lightspeed, your growth is our priority. We invest in you with continuous learning opportunities, global mobility and benefits designed to support you—all within a driven, diverse and inclusive team that’s passionate about empowering our communities.

Please note that we ask applicants to disclose any criminal convictions, and we conduct criminal record checks as part of our hiring process for this role.

At Lightspeed, we carefully consider a wide range of factors when determining compensation, including your skill set, qualifications, experience, and market data. These considerations can cause your compensation to vary. We reasonably expect the total compensation for this position to be in the range of $115,000TO $135,000 CAD. Lightspeed also provides a variety of employee benefits including, but not limited to, medical, dental, wellness, life and disability insurance, RRSP plan and match, paid parental leave top-up, and paid time off.

Please note that this compensation information is solely for candidates hired to perform work within Ontario (update to BC if posted in Vancouver) and reflects the amount Lightspeed is willing to pay at the time of this posting. This role represents an existing vacancy at Lightspeed. Lightspeed uses artificial intelligence–enabled tools to support certain aspects of the recruitment process; all hiring decisions are made by our recruiting and hiring teams.

To all recruitment agencies: Lightspeed does not accept unsolicited agency resumes. If we have not directly engaged your company in writing to supply candidates for a specific vacancy, Lightspeed will not be responsible for any fees related to unsolicited resumes.

Lightspeed is a proud equal opportunity employer and we are committed to creating an inclusive and barrier-free workplace. Lightspeed welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the selection process.

Where to from here?

Obviously, this has to be mutually beneficial: we want you to step into a role you love, and we want to offer you a place you’re proud to come to every day. For a glimpse into our world check out our career page here.

Lightspeed is building communities through commerce, and we need people from all backgrounds and lived experiences to do that. We were founded in 2005, in Montreal’s gay village and our original members were all part of the LGBTQ+ community. The ethos of our business has been about inclusion from the very beginning, and we strive to provide a workplace where everyone belongs.

Who we are:

Powering the businesses that are the backbone of the global economy, Lightspeed's one-stop commerce platform helps merchants innovate to simplify, scale, and provide exceptional customer experiences. Our cloud commerce solution transforms and unifies online and physical operations, multichannel sales, expansion to new locations, global payments, financial solutions, and connection to supplier networks.

Founded in Montréal, Canada in 2005, Lightspeed is dual-listed on the New York Stock Exchange (NYSE: LSPD) and Toronto Stock Exchange (TSX: LSPD). With teams across North America, Europe, and Asia Pacific, the company serves retail, hospitality, and golf businesses in over 100 countries.

Lightspeed handles your information in accordance with our Applicant Privacy Statement.

Кто мы и что строим

Flowwow — международный маркетплейс подарков. Под ним работает Data Platform, которую мы построили с нуля: не «настроили managed-сервисы», а спроектировали и написали сами — от storage-слоя до AI-агентов поверх данных.

Это lakehouse на Apache Iceberg + Trino, со Spark для тяжёлых вычислений, CDC-пайплайнами на Kafka/Debezium, всё в Kubernetes. Поверх этого — большой слой собственного кода:

• свой фреймворк Data Vault — моделирование хранилища как код, а не как набор ручных SQL-скриптов;

• обвязка вокруг Airflow — собственные операторы, хуки, генерация DAG'ов, инструменты тестирования пайплайнов;

• самописный lineage и инструменты наблюдаемости данных;

• AI-слой: агенты на LangGraph + MCP, которые расследуют инциденты, анализируют качество данных и помогают команде в рутине.

Мы — команда инженеров, а не операторов ETL. Бо́льшая часть нашей работы — это Python-разработка: библиотеки, фреймворки, сервисы, которыми пользуется вся дата-вертикаль компании.

Почему эта роль интересная

Это позиция Senior DE с сильным уклоном в платформенную разработку. Не «писать пайплайны по тикетам», а проектировать инструменты, из-за которых пайплайны пишутся в разы быстрее — или не пишутся руками вообще.

Вы будете работать с lakehouse-стеком на уровне внутренностей: оптимизация Trino-запросов и работа с коннекторами, maintenance Iceberg-таблиц (компакция, snapshot expiration, position/equality deletes — мы знаем эту боль изнутри), эволюция схем, REST-каталог. Это та глубина, которая редко встречается в продуктовых компаниях.

И второе: мы AI-native команда. Claude Code и аналогичные инструменты — часть ежедневного рабочего процесса, а не эксперимент по пятницам. Мы строим harness-системы вокруг LLM: агентов с инструментами, автоматизацию code review, ассистентов для написания DAG'ов. Если вам близко состояние, когда ты проектируешь систему, а агент под твоим контролем выполняет рутину — вам у нас понравится.

Что нужно делать:

• Развивать ядро платформы: фреймворк Data Vault, обвязку Airflow, lineage-инструменты — проектировать API, которыми будут пользоваться другие инженеры, и нести за них ответственность.

• Писать библиотеки и фреймворки на Python: клиенты к источникам, инструменты тестирования пайплайнов, обёртки над инфраструктурой. Чистый код, типизация, тесты, ревью — это у нас норма, а не аспирация.

• Работать с lakehouse на глубине: Iceberg maintenance и оптимизация layout'а таблиц, тюнинг Trino, Spark-джобы для тяжёлых трансформаций.

• Строить интеграции с источниками: OLTP-базы, REST/gRPC API, Kafka, файловые хранилища, SaaS-системы — и проектировать унифицированные интерфейсы, чтобы подключение нового источника занимало часы, а не недели.

• Внедрять AI в инженерные процессы: агенты для расследования инцидентов и анализа качества данных, ассистенты для DAG'ов и code review. У нас уже есть работающая агентная инфраструктура (LangGraph, MCP) — её можно и нужно развивать.

• Участвовать в архитектурных решениях: выбор технологий, дизайн интерфейсов между компонентами, вынесение best practices в команду.

• Делать code review и менторить менее опытных коллег.

Наши ожидания:

• Промышленный опыт от 5 лет в data engineering или backend-разработке с сильным дата-уклоном: production-системы под реальной нагрузкой, ответственность за их эксплуатацию, а не только за написание кода.

• Python — главное требование. Уровень senior разработчика: ООП, проектирование API библиотек, типизация, тесты, опыт написания переиспользуемых компонентов, которыми пользовались другие люди.

• Lakehouse-стек на практике. Apache Iceberg (или Delta/Hudi с готовностью быстро переключиться): устройство таблиц, snapshots, компакция, schema evolution. Понимание, чем lakehouse отличается от классического DWH не на уровне статьи на Medium.

• Trino или другой MPP-движок: архитектура распределённого выполнения, чтение планов запросов, оптимизация, опыт работы с коннекторами.

• Apache Airflow глубоко: не только разработка DAG'ов, но и внутреннее устройство (scheduler, executors, метаданные), опыт написания собственных операторов и хуков.

• Apache Spark: PySpark, Spark SQL, понимание модели выполнения.

• SQL продвинутого уровня: оконные функции, CTE, оптимизация, чтение планов исполнения, работа с большими объёмами.

• Git, CI/CD — уверенно.

Будет сильным плюсом

• Опыт разработки внутренних платформенных инструментов или фреймворков, которыми пользуются другие команды.

• AI-native подход к разработке: активное использование Claude Code / Codex и подобных инструментов, опыт построения harness-систем — агентов с инструментами, автоматизации через LLM, RAG.

• Kubernetes: деплой и эксплуатация дата-сервисов, Helm, понимание ресурсной модели.

• Опыт с CDC (Debezium, Kafka Connect) и стриминговыми пайплайнами.

• Знание форматов хранения (Parquet: row groups, статистики, predicate pushdown).

• Опыт работы с системами контроля доступа к данным (OPA, row-level security, маскирование).