- 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 solid 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

📌 Senior Platform Engineer (Data) (Toronto)
🏢 Mistplay
📍 Toronto