Senior Platform Engineer (Data)

Join Mistplay as a Senior Platform Engineer (Data) and be responsible for building and operating core systems that enable reliable, scalable, and high-velocity data access and analytics. You will own significant platform components, contribute to technical direction, and apply best practices that drive real-time business impact. This role requires strong experience in data platform engineering, data warehousing, and software engineering.

• Concevoir, construire et maintenir des systèmes d'ingestion de données évolutifs et fiables pour les sources de données par lots et en continu.
• Implémenter et faire évoluer la plateforme analytique de données (entrepôt, lac de données ou hybride) et contribuer aux normes de modélisation des données.
• Évaluer et intégrer des composants de plateforme de données (par exemple, Spark, dbt, Airflow, Kafka) et contribuer aux migrations et aux améliorations de la plateforme.
• Data Platform Experience - 7-8+ years building and operating production data platforms; proven ownership of components within large-scale systems supporting real-time or near real-time data access and analytical workloads.
• Streaming & Batch Pipelines - strong experience designing and operating data pipelines; solid understanding of streaming systems (e.g. Kafka, Flink) and batch frameworks (e.g. Spark, dbt) with awareness of trade-offs across latency, throughput, and cost
• Data Warehousing & Lakehouse - solid expertise in modern data warehouse and lakehouse architectures (e.g., Snowflake, BigQuery, Databricks, Delta Lake, Iceberg); experience building and optimizing analytical systems at scale
• Observability & Operations - solid operational rigor across data systems (metrics, logs, data quality alerts); experience contributing to SLO definitions, cost optimization, and incident response for data reliability
• Data Modeling & Transformation - ability to apply and contribute to data modeling standards across diverse consumer needs; experience working within transformation frameworks and testing practices across engineering and analytics teams
• Software Engineering - strong proficiency in Python, Scala, or Go; track record of building and evolving distributed data systems with high reliability, maintainability, and strong engineering standards
• Technical Growth (Senior Level) - actively participates in design reviews and architectural discussions; mentors teammates; demonstrates ownership of complex platform components; shows clear trajectory toward setting broader technical direction
• Collaboration & Influence - works effectively across Data Science, ML Platform, Analytics, DevOps, and Backend; communicates technical trade-offs clearly; translates requirements into well-scoped, executable platform work