Data Engineer - Financial Engineering

Remote

Job Description

We are looking for a mid level Data Engineer to join Spotify’s Finance Engineering organization. The Crunch squad builds and operates data systems that power financial forecasting, performance analysis, and insights tooling across Spotify. The team owns key parts of forecast production, including statistical and machine learning models, data pipelines, measurement tooling, infrastructure, and integrations across forecasting systems. This is an engineering-first role with high expectations for data quality, reliability, and long-term maintainability.

What You'll Do

Work with modern data processing frameworks, platforms, and tooling (Python, Scala, Java, Scio, Flyte, Styx, DBT, etc. on GCP) to build, operate and own reliable data pipelines and systems that support financial forecasting and performance analysis

Take ownership of existing financial and forecasting datasets, evolving schemas and data models to support new features and analytical use cases while protecting downstream consumers

Partner closely with engineers, product managers, and finance stakeholders to translate forecasting, planning, and variance analysis needs into well-designed solutions

Help improve automated financial controls to ensure data quality and compliance across our systems while continuously evaluating performance

Work in cross-functional, agile teams with end-to-end responsibility for the evolution and operation of long-lived data products

Who You Are

You have 3+ years of experience in building production-quality data solutions with complex business domain logic

You have 3+ years of experience using SQL and Python and are comfortable using data to troubleshoot issues and support business decision-making

You have experience owning data pipelines and datasets end to end, with accountability for maintainability, reliability, and safe evolution over time

You are a self-motivated contributor who can prioritize and deliver on projects while communicating effectively in a changing environment

You care deeply about data quality and have implemented monitoring, validation, and reliability best practices in real systems

You have experience working cross-functionally with engineers, product managers, and non-technical stakeholders

You value strong engineering practices, including testing, observability, and operational responsibility

Forecasting or Financial experience is a plus

Where You'll Be

This role is based in Toronto.

We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.

The Canadian base range for this position is 94,821 - 135,458 plus equity.

Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.

At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.

Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.

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