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 (in comparison, Google processes 8.5 billion searches per 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 effective 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 as (reporting, billing, MLO teams)

Operate across batch (Spark) and streaming (Kafka / Flink) architectures

Require careful balancing of:

data correctness

processing efficiency

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

near real-time updates

Evolving pipelines from centralized processing (Hadoop) toward more distributed and efficient patterns

Streaming pipelines and Streaming DWs.

What we’re lookingfor

Strong experience in data engineering at scale

Deep expertise in:

Distributed Data Processing frameworks

SQL and data modeling

Leading Projects end to end

Experience with:

Airflow or workflow orchestration

Kafka or streaming systems

Managing kubernetes

Strong understanding of:

distributed data processing

data modeling (large-scale datasets)

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

scalability

Here’s what you’ll be doing
Data Pipelines (Batch + Streaming)

Design and operate pipelines using:

Spark, Flink etc.

Kafka / Flink (streaming)

Transform raw event data into:

cleaned datasets (silver layer)

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

backfills

Improve reliability and observability of workflows

Data Processing Optimization

Optimize pipelines for:

performance (runtime, throughput)

cost (compute efficiency)

scalability (data growth)

Improve:

partitioning strategies

data layout

job execution patterns

Streaming & Near Real-Time Pipelines

Build pipelines that support:

incremental updates

streaming transformations

aggregation at scale

Contribute to evolving patterns such as:

edge aggregation

streaming batch convergence

real-time data availability

Platform & System Design Responsibilities

Define and evolve data processing patterns :

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📌 Staff Engineer, Data Platform & Infrastructure (Ontario)
🏢 Index Exchange
📍 Ontario