M
Mistplay
Senior Platform Engineer (Data)
Skills
Data EngineeringPythonGolangData ScienceKafkaSnowflakeDatabricksData AnalysisMachine LearningScaladbtApache Airflow
1 month ago
Share this opportunity
Share this opportunity to other talents of your network:
✓ Offer them a visibility boost with clients.
✓ Help your contacts find their next job.
Important information
Contract type:
Permanent
Location:
Toronto, ON, Canada
Work mode:
Hybrid
Published on:
11 September 2026
What they need
- Reporting to the Director of Data Platform, the Senior Data Platform Engineer is responsible for building and operating the core systems that enable reliable, scalable, and high-velocity data access and analytics across Mistplay
- This role is not about analysis - it is about engineering data systems at scale
- You will own significant platform components, contribute to technical direction, and apply best practices that enable data to move from raw ingestion to trusted, high-quality insights that drive real-time business impact
- You will operate as a strong individual contributor across teams, partnering with Data Science, ML Platform, and Backend to reduce data latency, increase analytical throughput, and improve data trust across the full data lifecycle
- Ingestion & Pipeline Infrastructure - build and maintain scalable, reliable ingestion systems for batch and streaming data sources; implement schema evolution, data contracts, and end-to-end lineage; contribute to compute and cost optimization across diverse workloads
- Data Warehouse & Lakehouse Architecture - implement and evolve the core analytical data platform (warehouse, lakehouse, or hybrid); apply storage layer strategies, partitioning, and access patterns; contribute to data modeling standards, performance tuning, and cost efficiency
- Transformation & Orchestration Layer - build and maintain scalable, maintainable transformation pipelines (e.g., dbt, Spark); implement orchestration and dependency management; enforce data quality contracts and testing frameworks across the transformation layer
- Data Serving & Access Layer - implement low-latency data access systems for analytical and operational consumers; apply caching, materialization, and API strategies; contribute to SLAs on freshness, consistency, and query performance
- Observability & Data Quality - implement data quality monitoring, anomaly detection, and freshness checks; contribute to data SLO definitions and operational practices; participate in incident response and postmortems for data reliability
- Data Catalog & Discoverability - contribute to metadata management systems; drive data discoverability, ownership, and documentation standards within your domain; support self-serve access to trusted, well-understood data assets
- Platform Tooling & Evolution - evaluate and integrate data platform components (e.g. Spark, dbt, Airflow, Kafka, data catalogs); contribute to migrations and platform improvements with minimal disruption to downstream consumers- 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
Other offers great for you!
These companies are also looking for great profiles
T
Teksystems
Site Reliability Engineer (SRE) (H/F)
Freelance
92130 Issy-les-Moulineaux, France
Hybrid
Skills
sre
6 months ago
Quick apply
W
Whub
Site Reliability Engineer (SRE) (H/F)
500€/jour - 650€/jour
Freelance
Lille, France
Onsite
Skills
PythonDatadogLinuxTerraform
5 months ago
Quick apply
C
Cherrypick
Site reliability Engineer (SRE)
550e/jour
Freelance
Paris, France
Hybrid
Skills
GrafanaAWSKubernetesLinux/UnixCI/CD
3 days ago
Quick apply
No company information available.