Staff Engineer, Data Platform & Infrastructure in Toronto

At Index Exchange, we’re reinventing how digital advertising works—at scale. As a global advertising supply-side platform, we empower the world’s leading media owners and marketers to thrive in a programmatic, privacy-first ecosystem. We’re a proud industry pioneer with over 20 years of experience accelerating the ad technology evolution. Our proprietary tech is trusted by some of the world’s largest brands and media owners and plays a crucial role in keeping the internet open, accessible, and largely free.

We process more than 550 billion real-time auctions every day with ultra-low latency. Our platform is vertically integrated from servers to networks and runs primarily on our own metal and cloud infrastructure. This end-to-end infrastructure is designed to provide both stability and agility, enabling us to adapt quickly as the market evolves.

At the core of it all is our engineering-first culture. Our engineers tackle internet-scale problems across tight-knit, global teams. From moving petabytes of data and optimizing with AI to making real-time infrastructure decisions, Indexers have the agency and influence to shape the future of advertising. We move fast, build thoughtfully, and stay grounded in our core values.

About the role: We are hiring a Staff Data Engineer to build and evolve the data processing and pipeline layer that powers reporting, billing systems, and real-time data streams at Index Exchange. This role focuses on designing and operating large-scale batch and streaming data pipelines, enabling reliable, scalable, and efficient data transformation across the platform.

You will work on systems that transform raw, high-volume event data into clean, queryable, and production-grade datasets. You will work on high-scale data systems that:

• Process billions of events per day across distributed pipelines
• Power core data platform supporting consumers such as reporting, billing, and MLO teams
• Operate across batch (Spark) and streaming (Kafka / Flink) architectures
• Require careful balancing of data correctness, processing efficiency, and latency vs cost trade-offs

You will solve problems such as:

• Designing pipelines that scale without exploding compute costs
• Managing data correctness at scale (deduplication, late data, joins)
• Building systems that support both historical backfills and near real-time updates
• Evolving pipelines from centralized processing (Hadoop) toward more distributed and efficient patterns
• Streaming pipelines and Streaming DWs.

What we’re looking for:

• Strong experience in data engineering at scale
• Deep expertise in Distributed Data Processing frameworks and SQL and data modeling
• Leading projects end to end
• Experience with Airflow or workflow orchestration, Kafka or streaming systems, and managing Kubernetes
• Strong understanding of distributed data processing, data modeling (large-scale datasets), and performance optimization

Ability to:

• Own pipelines end-to-end
• Debug complex data issues
• Work in high-scale, evolving environments

Staff-Level Expectations:

• Define data processing standards and patterns across teams
• Lead large-scale pipeline and platform initiatives
• Influence data architecture and modeling decisions
• Drive improvements across reliability, cost efficiency, and scalability

Here’s what you’ll be doing:

• Data Pipelines (Batch + Streaming): Design and operate pipelines using Spark, Flink, etc.
• Transform raw event data into cleaned datasets (silver layer) and business-ready datasets (gold / reporting tables)
• Core Data Models & Datasets: Build and maintain canonical datasets (aggregated datasets, reporting tables)
• Define data contracts and ensure consistency across pipelines
• Support evolving use cases: reporting, billing, ML / experimentation
• Workflow Orchestration: Build and maintain Airflow DAGs for pipeline scheduling, dependency management, and backfills
• Improve reliability and observability of workflows
• Data Processing Optimization: Optimize pipelines for performance (runtime, throughput), cost (compute efficiency), and scalability (data growth)
• Improve partitioning strategies, data layout, and job execution patterns
• Streaming & Near Real-Time Pipelines: Build pipelines that support incremental updates, streaming transformations, and aggregation at scale
• Contribute to evolving patterns such as edge aggregation, streaming batch convergence, and real-time data availability

Platform & System Design Responsibilities: Define and evolve data processing patterns.