1 month ago
Toronto, CanadaSenior
Responsibilities
- Design and implement scalable ETL and ELT pipelines from diverse sources into analytical data stores.
- Develop and maintain logical and physical data models supporting multiple product and analytics use cases.
- Define and enforce data quality frameworks with validation checks, monitoring, and upstream logging improvements.
- Establish data access, privacy, and security models aligned with privacy requirements and data governance standards.
- Build data staging solutions and interconnected visualization interfaces for self-service exploration.
- Contribute to semantic services and metadata frameworks that standardize data definitions.
- Write, debug, and optimize SQL and programmatic queries against large-scale distributed query engines.
- Translate business requirements into robust data infrastructure solutions with cross-functional partners.
- Identify gaps and propose standards, policies, and processes for data governance and metadata management.
- Improve shared data infrastructure through targeted enhancements, peer enablement, and documentation.
Requirements
- At least 5 years of experience in data engineering, including production-grade ETL or ELT pipeline design and implementation.
- At least 5 years of experience writing and optimizing SQL queries against large-scale distributed data systems.
- Experience designing logical and physical data models for multiple analytical use cases at scale.
- Experience implementing data quality processes, data access controls, and privacy-compliant data handling practices.
- Experience coding in Python, Scala, or a comparable language for maintainable and testable data pipelines.
- Preferred experience with responsible and ethical AI practices, including risk assessment, bias mitigation, and quality reviews.
- Preferred experience improving upstream data quality through application logging or structured instrumentation.
- Preferred experience building self-service analytics platforms or interconnected dashboard ecosystems.
- Preferred ongoing AI skill development, including prompt/context engineering and agent orchestration.
- Preferred experience with shared data infrastructure, semantic layers, or metadata management systems.
- Preferred familiarity with Apache Spark, Flink, or equivalent stream-processing frameworks.
- Preferred ability to integrate AI tools to improve workflows and measurable outcomes.
Benefits
- CA$131,000/year to CA$181,000/year plus bonus, equity, and benefits.
Tech Stack
Categories
Data Engineering
About Meta
Meta builds social platforms and communication apps—including Facebook, Instagram, WhatsApp, and Messenger—and develops AR/VR hardware and software such as Quest to power immersive computing. It monetizes primarily through advertising tools for businesses, with additional revenue from devices and services, and operates a massive global infrastructure. Founded in 2004 and headquartered in Menlo Park, California, Meta Platforms, Inc. is a public company traded on Nasdaq under the ticker META.