Staff Software Engineer, Big Data

Cognitiv•Vancouver, BC

1d•CA$170,000 - CA$240,000•Hybrid

About The Position

At Cognitiv, we are industry trailblazers redefining media buying with our Deep Learning Advertising Platform. Since 2015, we have harnessed the power of cutting-edge deep learning technology and data science to transform how brands connect with their customers. Our mission is to bring intelligence to advertising and deliver unparalleled precision, relevance, and impact at scale. With our innovative platform, advertisers enjoy unprecedented flexibility—whether it is activating Dynamic Deals through their preferred DSP, leveraging our managed service DSP, or utilizing our industry-first ContextGPT product. As a part of Cognitiv, you will be at the forefront of AI-driven advertising solutions, driving change and achieving remarkable growth in a rapidly evolving industry. As a Staff Software Engineer, Big Data, you will define and drive the long-term technical direction of the data platform that powers Cognitiv's AI-driven advertising products. You will lead the architecture and evolution of critical data systems spanning ingestion, warehousing, machine learning feature generation, identity resolution, and activation at massive scale. As a senior technical leader, you will identify systemic challenges, align teams around durable solutions, and drive complex initiatives that span engineering, science, machine learning, and product organizations. Your work will shape the scalability, reliability, and strategic capabilities of Cognitiv's data platform for years to come.

Requirements

Staff-Level Technical Leader. You bring 10+ years of experience building and evolving large-scale distributed systems, data platforms, or backend infrastructure, with a track record of influencing technical direction beyond a single team.
Expert in Big Data Systems. You have deep experience designing and operating large-scale data processing platforms using technologies such as Spark, Kafka, Flink, Iceberg, ClickHouse, or similar distributed systems.
Architectural Thinker. You have led architectural decisions for complex systems and can balance scalability, performance, reliability, maintainability, and business needs when making technical tradeoffs.
Cross-Team Influencer. You have successfully driven complex initiatives across multiple teams and functions, aligning stakeholders around durable technical solutions without direct authority.
Cloud-Native Systems Builder. You have extensive experience building and operating data-intensive systems in cloud environments such as AWS, Azure, or GCP.
Strategic Problem Solver. You identify systemic technical challenges, proactively create solutions, and drive execution with minimal guidance.
Mentor and Multiplier. You elevate the effectiveness of engineers around you through mentorship, technical leadership, and establishing engineering best practices.
Exceptional Communicator. You effectively communicate technical strategy, architectural decisions, and tradeoffs to engineering leaders, product stakeholders, and executive audiences.

Nice To Haves

Experience designing, scaling, and maintaining petabyte-scale data platforms
Expertise in distributed systems architecture and large-scale data processing
Experience building ML infrastructure, feature stores, or feature engineering platforms
Experience driving platform modernization or data architecture transformations
Experience operating in high-growth startup or scale-up environments
Deep familiarity with Flink, ClickHouse, Kafka, Iceberg, or similar modern data technologies

Responsibilities

Lead Platform Architecture: Define and evolve the architecture of Cognitiv's large-scale data platform, ensuring ingestion, storage, processing, and serving systems can scale to support future business growth, AI initiatives, and increasing data volumes.
Drive Multi-Team Data Initiatives: Lead complex, cross-functional programs spanning Data Engineering, Machine Learning, Science, and Product teams. Align stakeholders, reduce execution risk, and deliver outcomes that improve platform capabilities across the organization.
Shape Long-Term Technical Direction: Evaluate architectural tradeoffs with a 3–5 year horizon, establish technical standards and paved-road solutions, and drive modernization efforts that improve scalability, reliability, developer productivity, and operational efficiency.
Elevate Reliability and Operational Excellence: Lead architecture reviews, failure-mode analysis, and incident retrospectives. Establish mechanisms to proactively identify, prioritize, and reduce technical risk across critical data systems.
Own Cost and Efficiency at Scale: Treat cost as a first-class architectural dimension for a multi-petabyte data platform. Lead cost vs. performance tradeoff decisions across storage tiering, compute, file layout, and data movement, and build the visibility and guardrails that let teams reason about unit economics when they design systems.
Advance Data and ML Platform Capabilities: Guide the evolution of foundational systems including the identity graph, ML feature platform, feature projection infrastructure, warehouse architecture, and large-scale distributed processing frameworks.
Partner on Strategic Priorities: Collaborate with Engineering, Product, Science, and Machine Learning leadership to align platform investments with business objectives, balancing technical debt, platform health, and product velocity.
Grow Engineering Talent: Mentor senior engineers and emerging technical leaders. Raise engineering standards through design reviews, architectural guidance, technical artifacts, and thoughtful coaching.