Staff Software Engineer - Data Delivery

Location: Canada; United States #LI-REMOTEFull-TimeStaff

Salary not disclosed

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Job Details

Required Skills: KafkaGoRESTful APIsReportingMentorshipRedshiftDistributed Systems

Strong experience building scalable services, distributed systems, data platforms, or data-intensive applications.
Experience leading complex technical projects with ambiguous requirements, multiple stakeholders, and meaningful trade-offs.
Strong judgment across APIs, data pipelines, databases, distributed systems, observability, reliability, and operational ownership.
Ability to reason about correctness, freshness, completeness, consistency, cost efficiency, and customer trust.
Interest in building systems that support customer-facing reporting, measurement, planning, optimization, and analytics.
Ability to explain decisions clearly, align stakeholders, and document important trade-offs.
Experience helping other engineers grow and raising the technical bar of a team.
Strong programming skills; experience with Golang and technologies such as Kafka, TiDB, Redshift, Vitess, Iceberg, StarRocks, or Trino is a plus.
Experience in adtech, marketing technology, reporting, analytics, billing, attribution, planning, or high-volume event processing is a plus.

Act as a project and domain DRI for measurement and planning initiatives within Data Delivery.
Translate product requirements into technical requirements, clarify feasibility and trade-offs, shape milestones, and identify risks early.
Keep complex initiatives moving by unblocking decisions, coordinating across teams, and giving engineering leadership clear options and recommendations.
Build reliable systems that process, organize, and serve large volumes of campaign and marketing data across StackAdapt.
Power reporting, measurement, planning, billing, pacing, exports, APIs, and analytics that customers and internal teams depend on.
Make thoughtful technical decisions that balance correctness, reliability, latency, freshness, cost, and long-term maintainability.
Work closely with product, engineering, data science, analytics, and business teams to turn product goals into strong technical solutions.
Improve monitoring, testing, data quality, incident response, and documentation so our systems are easier to trust and operate.
Support engineers through design reviews, code reviews, technical guidance, and clear communication of trade-offs.

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