Bandsintown powers live-music discovery for over 100M fans and 700,000 artists . Our data platform drives real-time event intelligence, artist analytics, and large-scale marketing automation across the global live-music ecosystem.

We’re expanding our distributed data engineering team to build the next generation of high-throughput ingestion , streaming , and real-time serving systems .

If you love Spark , Airflow , AWS , and building resilient data platforms , you’ll feel right at home.

THE ROLE

You’ll design, build, and operate the distributed systems that move and transform mission-critical data across Bandsintown. You’ll work spec-first , own your components end-to-end, and build pipelines that are idempotent, restartable, observable, and built for scale .

This is a hands-on role for engineers who thrive in high-volume, real-time environments and want to see their work directly impact millions of users.

WHAT YOU WILL DO

Build streaming & Distributed systems

- Create high-throughput pipelines using Spark, Kinesis, EMR, Glue, and AWS serverless.
- Build restartable, observable Airflow DAGs for both batch and streaming workloads.
- Implement real-time ingestion with proper partitioning, offset management, watermarking, and backpressure.

Engineer for scale & reliability

- Architect systems for high availability, horizontal scalability, and real-time serving.
- Define SLIs/SLOs, instrument everything with CloudWatch, structured logs, and Grafana dashboards.
- Build dashboards that answer: “Is the data product actually working?”

Own AWS data platform components

- Build pipelines using Airflow, Kinesis, EMR/EMR Serverless, Glue, Lambda, ECS, Athena.
- Implement serverless ingestion,



event-driven architectures, and distributed compute.
- Enforce IAM least privilege, Secrets Manager, and dependency hygiene (Snyk, Dependabot).

Work spec-first with AI assistance

- Write clear technical specs before coding.
- Use Claude Code, Cursor, Copilot to accelerate development while keeping architecture tight.
- Maintain Backstage entries and service docs as living artifacts.

Collaborate & take ownership

- Partner with product, architecture, DevOps, BI, and Data Science.

WHAT YOU BRING

Required

- 5+ years building large-scale distributed systems and data pipelines.
- Deep experience with: Spark ; Airflow (idempotent, restartable DAGs) ; AWS ingestion stack (Kinesis, EMR, Glue, Lambda, ECS, CloudWatch).
- Strong programming skills in Python, PySpark .
- Strong SQL and experience with PostgreSQL, MySQL, Redshift , or similar.
- Solid distributed systems fundamentals (partitioning, consistency, backpressure).
- Experience with observability : CloudWatch, Grafana, structured logs, alerting.
- Experience with CI/CD (Buildkite, GitHub Actions, Jenkins).
- Comfortable using AI coding tools in a spec-first workflow.

Nice-to-Have

- Experience supporting ML/AI pipelines.
- Experience with Snowflake, Iceberg, dbt, Druid, Trino.
- Experience with DataHub or similar catalogs.
- A passion for live music.

WHY BANDSINTOWN

- Build systems used by 100M+ fans and 700,000 artists.
- Join a high-leverage, AI-native engineering culture.
- Work in a small, senior team where your components matter.
- 4 weeks vacation, plus versatile summer hours.
- Full health coverage from day one.
- A human-centered culture with real ownership, creativity, and room to grow.

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📌 Développeur/euse back-end Big Data (Montreal)
🏢 Bandsintown Group
📍 Montreal