Développeur/euse back-end Big Data

ABOUT BANDSINTOWN 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. Own your components from design → deployment → monitoring → incident response. 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. Bilingual French + English. 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. Hybrid model: 2 days in our Montréal office, 3 days remote. 4 weeks vacation, plus flexible summer hours. Full health coverage from day one. A human-centered culture with real ownership, creativity, and room to grow.

Design, build, and operate distributed systems and high-throughput data pipelines for real-time event intelligence. Own components end-to-end from technical specification and development to deployment and monitoring.

Requires 5+ years of experience building large-scale distributed systems with deep expertise in Spark, Airflow, and the AWS data stack. Must be proficient in Python and SQL and be bilingual in French and English.

• 4 Weeks Vacation • Flexible Summer Hours • Full Health Coverage

• PostgreSQLSouhaitée

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Spark
• Airflow
• AWS
• Python
• PySpark
• SQL
• Kinesis
• EMR
• Glue
• Lambda
• ECS
• CloudWatch
• Grafana
• CI/CD
• Distributed Systems
• PostgreSQL

• Data & Analytics
• Software
• Technology
• Engineering

Expérience minimale

5+ ans

Postuler avant le

28 août 2026

Langue de l’offre

anglais

Heures de travail

40 heures par semaine

Présence au bureau

2 jours par semaine

Niveau d’expérience

Associate

Mode de candidature

La candidature directe est offerte