Job description

Who We Are DV is the leader in digital performance solutions, helping our advertiser and agency partners Verify the quality of their digital campaigns, Optimise to improve performance and Prove that they’re achieving their business outcomes, through unbiased 3rd party data and analytics. DV’s mission is to be the definitive source of transparency and data-driven insights into the quality and effectiveness of digital advertising for the world’s largest brands, agencies, publishers, and digital ad platforms. Since 2008, DV has helped hundreds of Fortune 500 companies gain the most from their media spend by delivering best-in-class solutions across the digital advertising ecosystem, helping to build a better industry. Learn more at www.doubleverify.com . Within DV, Rockerbox empowers marketing executives to confidently make data-driven decisions, helping brands such as Tula, Figs, and Burton with the strategic decision-making that drives growth. To do so, Rockerbox offers a unique suite of product lines that centralize data and offer diversified measurement methodologies. The foundation of Rockerbox s solution is data centralization. Atop this foundation, the platform enables marketers to choose from a range of measurement methodologies, giving customers the flexibility to choose the most appropriate approach for their specific needs and questions. As a Senior Software Engineer II at Rockerbox, you will take technical ownership of key components within the data platform that powers our products. You ll drive execution across the foundation of our stack: from Kubernetes-based pipelines and aggregations, through our lakehouse, and into the semantic layer, ensuring data flows cleanly, reliably, and deterministically at scale. Partnering closely with product and platform teams, you ll define the contracts that connect these layers and strengthen the reliability and observability of the platform end to end. You ll also use modern AI to build efficiently and to a high standard, architecting systems that AI can build on. If you enjoy putting the pieces together across the stack, bringing order to the boundaries between layers, and building resilient data infrastructure with modern technologies, this is the role for you. What You ll Do Spearhead technical execution across the seam between our data foundation and semantic layer, ensuring data flows cleanly and reliably into downstream products and workflows. Design and develop scalable solutions that enhance application functionality, data efficiency, and overall system performance. Architect data endpoints using a semantic layer to transform source data into intuitive, optimized structures for consumption by our UI, CLIs, and AI agents. Implement advanced monitoring, data-quality checks, and operational practices across pipelines to accelerate incident detection and resolution. Operate as a senior generalist across the stack, taking full technical ownership of complex initiatives spanning both data infrastructure and application/API layers. Partner closely with product managers and cross-functional stakeholders (Data, Applications, Data Science, Customer Success) to define requirements and deliver reliable, timely data. Who You Are 7 years of experience in software engineering, with demonstrated success building, owning, and maintaining production systems that prioritize reliability, scalability, and performance. Proven expertise in contract-driven design, API design, and semantic/serving layers built on top of a datalake or data warehouse. Hands-on mastery with datalake and warehouse technologies: columnar formats (Parquet), query engines (DuckDB), object storage (S3-compatible), lakehouse table formats (e.g., Iceberg), and cloud warehouses (e.g., Snowflake). Fluency in at least one general-purpose language (we use Python and Go) and strong SQL, with the judgment to pick the right tool rather than a deep attachment to any one language. Comfortable using AI as a primary tool (coding assistants, LLMs, and agents), and eager to build the contracts and interfaces that let AI systems operate on our data safely. Proven track record in monitoring and observability, and a practical instinct for data quality. Fluency with orchestration, containerization, and Kubernetes for scalable deployments. Energized by working at the boundary between our data foundation and the products built on it, and by defining the interfaces that connect them. Flexible and self-motivated, with a strong drive to see problems through to a solution. Effective communicator, capable of clearly conveying complex technical concepts to non-technical stakeholders. Nice to Have Experience with observability tooling (Prometheus, Grafana, OpenTelemetry) and data-quality frameworks. Familiarity with marketing analytics, MTA, MMM, testing, or customer data platforms. The successful candidate’s starting salary will be determined based on a number of non-discriminating factors, including qualifications for the role, level, skills, experience, location, and balancing internal equity relative to peers at DV. The estimated salary range for this role based on the qualifications set forth in the job description is between [$107,000 - $193,000]. This role will also be eligible for bonus/commission (as applicable), equity, and benefits. The range above is for the expectations as laid out in the job description; however, we are often open to a wide variety of profiles, and recognize that the person we hire may be more or less experienced than this job description as posted. Not-so-fun fact: Research shows that while men apply to jobs when they meet an average of 60% of job criteria, women and other marginalized groups tend to only apply when they check every box. So if you think you have what it takes but you’re not sure that you check every box, apply anyway!

Requirements

Must-haves

7 years of experience in software engineering, with demonstrated success building, owning, and maintaining production systems that prioritize reliability, scalability, and performance.
Proven expertise in contract-driven design, API design, and semantic/serving layers built on top of a datalake or data warehouse.
Fluency in at least one general-purpose language (we use Python and Go) and strong SQL, with the judgment to pick the right tool rather than a deep attachment to any one language.
Comfortable using AI as a primary tool (coding assistants, LLMs, and agents), and eager to build the contracts and interfaces that let AI systems operate on our data safely.
Proven track record in monitoring and observability, and a practical instinct for data quality.
Fluency with orchestration, containerization, and Kubernetes for scalable deployments.
Energized by working at the boundary between our data foundation and the products built on it, and by defining the interfaces that connect them.
Flexible and self-motivated, with a strong drive to see problems through to a solution.
Effective communicator, capable of clearly conveying complex technical concepts to non-technical stakeholders.

Nice-to-haves

Experience with observability tooling (Prometheus, Grafana, OpenTelemetry) and data-quality frameworks.
Familiarity with marketing analytics, MTA, MMM, testing, or customer data platforms.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

kubernetesaiapipythongosql

Platform: workday
Effort: long