Dev Backeng Bid Data

Bandsintown connects 100 million fans with live music: following artists, discovering concerts, and buying tickets. We are the leading concert discovery platform, powered by over 700,000 artists connected to the platform and integrated with Spotify, YouTube, Apple Music, and major ticketing platforms.

We are now expanding into live event production, leveraging our audience data and distribution to build a scalable and profitable entertainment business. This marks our first major product expansion beyond the platform.

Backed by:

• Leading investors in the entertainment and technology sectors

• Team: Over 90 employees across North America, Asia, and Europe

• Culture: Data-driven, entrepreneurial, passionate about music

We are looking for a skilled Backend Developer to join our Development team in Montréal. You will be a key player on the team responsible for our core events and data platforms (designing, building, and maintaining the distributed systems that power our business).

This role is for a hands-on developer who loves solving complex challenges in scalability, real-time processing, and data management. You will work spec-first: every significant piece of work begins with a published technical spec in Confluence, and you’ll use AI-assisted development workflows (Claude Code, Cursor) to execute against those specs efficiently and safely.

You'll collaborate with product owners, architects, and data scientists across the company to build data products from the ground up. If you are passionate about music, technology, and building robust, large-scale systems that handle massive amounts of data, this is the perfect opportunity for you.

• Design & build: Design, develop, and maintain core backend and data systems, focusing on batch and stream processing for data-intensive applications. Every new component begins with a published spec (Confluence) and a Backstage entry.  Documentation is part of the product, not an afterthought.

• Scale systems: Build for scalability, high availability, and real-time serving. Define SLIs and SLOs for your components and measure reliability over time in CloudWatch SLO.

• Data pipelines: Develop and optimize complex ETL/ELT data pipelines using tools like Spark, Kinesis, and Airflow. All DAGs are idempotent, restartable, and observable by default. You will own that bar.

• Observe and operate: Instrument every component with CloudWatch metrics and structured logs, ship Grafana dashboards that reflect real data-product health (not just infrastructure), and define CloudWatch alarms in code. You are on the pager; your dashboards must be useful under incident pressure.

• Write quality code: Produce well-structured, efficient, scalable, and thoroughly tested code. For Tier 0 and Tier 1 components, mutation testing is part of CI.

• Work spec-first with AI assistance: Author and publish technical specs in Confluence before building. Use Claude Code and Cursor to accelerate implementation against those specs. Follow the spec-first agentic development workflow (including gap and deviation feedback loops) so AI-produced code stays aligned with architecture and contracts.

• Security and dependency hygiene: Own the security posture of your components. Run Snyk in CI, keep Dependabot PRs reviewed weekly, store all secrets in AWS Secrets Manager, and enforce IAM least-privilege on every runtime identity you create.

• Collaborate & communicate: Work in a cross-functional environment with product owners, architects, DevOps, other developers, and the BI and Data teams. Ask “why” to understand product goals and translate functional requirements into technical specs.

• Own your components: Take full ownership of your components from design through deployment and monitoring. A component is not done when code ships, it is done when it is observable, documented, and operable.

• Experience: 5+ years of practical experience in backend software development, building large-scale, highly available, and distributed data systems.

• Data processing, hands-on experience with modern big data technologies (Spark, Kafka, Hadoop, Airflow, or similar). You understand idempotency, partitioning strategies, and what it means to make a DAG safely restartable.

• Programming: Solid background in Python, PySpark, or Java.

• Databases: Strong knowledge of SQL and experience with various data stores (PostgreSQL, MySQL, Redshift). You understand data ownership boundaries and know why direct cross-component database reads are an anti-pattern.

• Search: Experience with Elasticsearch or Solr.

• Cloud & DevOps: Proven experience with AWS (serverless, ECS, S3, RDS, EMR, EMR Serverless, Kinesis, Athena, Glue). Experience with Buildkite or equivalent CI/CD pipelines.

• Observability: Comfortable with CloudWatch metrics, structured logging, Grafana dashboards, and alert design. You build dashboards that answer “is the data product actually working?” not just “is the process up?”

• AI-Assisted development: Experience using AI coding tools (Claude Code, Cursor, GitHub Copilot, or similar) in a production workflow. You understand the importance of spec-first development and human review of agent-generated code.

• Security awareness: Familiar with dependency scanning (Snyk), secrets management, and IAM least-privilege patterns. You do not commit secrets.

• CS fundamentals: Strong foundation in data structures, algorithms, and complexity analysis.

• Documentation-first mindset: You write specs before you write code. You maintain backstage entries, confluence service docs, and AGENTS.md files as living artifacts, not one-time deliverables.

• User-obsessed & deeply curious: You are obsessed with the end-user experience, whether for your internal teammates or our diverse external users (including fans, artists, venues, promoters, and strategic partners). You relentlessly ask "why" to understand the core business drivers and user needs behind every technical request. You have excellent communication skills, thrive in a collaborative environment built on frequent feedback, and genuinely enjoy working with diverse teams.

• Language: Bilingual (French and English) is essential for our Montréal team.

• Experience supporting Data Science or Machine Learning workflows.

• Experience building or evaluating LLM-powered features or AI-driven data products.

• Data governance and quality: knowledge of data quality checks, lineage tracking, and data cataloging (DataHub or equivalent).

• Familiarity with spec-to-audience documentation patterns, transforming technical specs into onboarding guides, product briefs, or executive summaries.

• A passion for music!

• Work on a platform that 100M+ music fans and 700,000 artists use and love.

• Be part of a small, high-leverage team: your components matter and you'll know it from the dashboards.

• AI-native engineering culture: we use AI as a multiplier, not a shortcut. Specs, skills, and feedback loops are engineering artifacts here.

• Fast-paced, international environment where you can learn and grow.

• Vacation: 4 weeks, which you can take right from the start without having to accrue them.

• Schedule: 40 hours, except in July and August when we switch to 36 hours with “Summer Fridays” (no work in the afternoon!).

• Work style: hybrid, travel, in-office, remote…

• Health insurance: Active from day one! The employer pays 60% and you pay 40%. It covers prescription drugs, dental care, specialists, travel, and disability (short- and long-term).

• Stipends: $15 per month for your music streaming subscriptions (cool!); $200 per year to treat yourself to concert tickets.

• RRSP: The plan is currently being revised to be more advantageous, starting from your first year.