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The Trade Desk is a global technology company and the world’s leading independent platform for digital advertising, with nearly 4,000 employees across more than 30 offices. Our technology helps advertisers reach the right audiences across the open internet — from streaming TV and podcasts to mobile apps, news, and more. Advertising powers the content people love. By making it more transparent, effective, and responsible, we help support trusted journalism, quality entertainment, and creators worldwide. The world’s brands and agencies rely on us to reach their customers and grow their businesses responsibly. The scale of our platform brings unique technical challenges — from processing massive datasets in real time to building systems that operate reliably on a global scale. When you work here, your impact is worldwide. We welcome diverse perspectives, encourage curiosity, and build teams that learn from one another. If you’re driven to solve meaningful challenges, we’d love to meet you. We are seeking a Software Engineer, Commercial Intelligence and Analytics to build high-impact analytical systems supporting TTD’s cross-functional teams, with a focus on Sales and Client Services. This role sits at the intersection of commercial analytics, applied intelligence, and go-to-market execution. You will identify system gaps, design solutions, and ship scalable data systems, insight pipelines, and automated workflows that support a broad set of internal stakeholders. Working across Commercial Operations, Revenue Operations, Business Technology, Finance, and related teams, you will help translate analysis into proactive signals that improve prioritization, decision-making, and field execution. This role owns the engineering lifecycle of intelligence systems from requirements and architecture through deployment, monitoring, and iteration. You will work closely with other teams to deliver clear, trusted, and actionable intelligence that improves seller focus, client outcomes, and the overall client experience at TTD. What We Do Commercial Operations is composed of closely connected functions that help TTD grow revenue in a disciplined, scalable, and data-driven way. Across these teams, we improve visibility, standardize key metrics, strengthen decision support, and help client-facing teams act with greater precision and consistency. Within this organization, Commercial Intelligence and Analytics turns commercial, product, and client data into insight products that support internal teams and improve how TTD serves, grows, and retains clients. We build data pipelines, signal frameworks, and decision-support systems that help Sales and Client Services teams understand portfolio health, prioritize effort, identify risk and opportunity earlier, and engage clients more effectively. Our work spans scalable reporting infrastructure, proactive signal delivery, and AI-assisted workflows that operationalize intelligence into durable, production-grade systems. Core mission The Software Engineer owns the design, development, and reliability of intelligence systems from requirements through production. This includes identifying where systems, automation, or infrastructure improvements can unlock better decision support. It also includes architecting insight pipelines, defining technical requirements, and building and deploying scalable solutions. The systems you build should be trusted, understandable, and useful. Success in this role is measured by the durability, reliability, and impact of the systems you ship. What You'll Work With Python, SQL, Snowflake, Tableau, Git/GitLab (CI/CD), LLM APIs, Docker, Jira, Confluence What You'll Do Own and maintain the production systems that surface revenue performance, portfolio health, renewal dynamics, client opportunity, and service risk so internal teams can act on the right priorities with confidence. Design, build, and operate proactive insight pipelines, translating recurring business needs into system architecture, data models, signal logic, and production-ready outputs so intelligence can be delivered consistently at scale. Work directly with Business Technology and Revenue Operations to integrate intelligence outputs into dashboards, alerting systems, and operational workflows so signals reach users through the tools where decisions are made. Build and deploy tools, signals, and automated deliverables that help teams prioritize effort, identify risk and opportunity, and act with greater precision so commercial resources are directed where they matter most. Build trusted, production-grade data assets and pipelines using SQL, Python, validated metrics, and well-documented logic so downstream systems remain reliable, explainable, and maintainable. Integrate AI and LLM capabilities into systems and workflows to accelerate exploration, automate documentation, detect anomalies, and improve pipeline quality so teams can move faster without sacrificing rigor. Translate ambiguous commercial problems into concrete technical plans, stakeholder-aligned requirements, and measurable deliverables. Identify and prioritize scalable system investments that are actionable and likely to drive broad commercial impact. Embed intelligence outputs into go-to-market systems and instrument delivery to assess whether signals reach the right users, inform action, and improve outcomes. Raise the team’s engineering bar through rigorous documentation, automated testing, metric governance, and repeatable, version-controlled workflows. Required Qualifications 5+ years of experience in analytics engineering, data engineering, software engineering, or a similarly systems-oriented role with strong quantitative foundations. Strong SQL skills and experience working with large, complex datasets in modern data environments. Proficiency in Python for building production systems, automation, and data pipeline development. Strong foundation in software development practices including version control (Git), code review, testing, and CI/CD pipelines. Experience translating business requirements into technical designs, system architecture, and production-ready deliverables. Experience building durable, automated systems rather than relying on ad hoc or manual processes. Demonstrated track record of identifying opportunities, proposing solutions, and independently driving systems, automation or technical initiatives from concept through adoption, often in ambiguous or loosely defined environments. Experience operating in technical environments while collaborating with a broad set of internal stakeholders, including client-facing teams. Familiarity with AI- or LLM-assisted tools and strong judgment in applying them responsibly and validating outputs to ensure accuracy, rigor, and reliability. Strong written and verbal communication skills, including the ability to explain findings and tradeoffs clearly to a range of stakeholders. Strong attention to data quality, metric consistency, and engineering rigor. Preferred Qualifications Experience supporting Sales, Client Services, Revenue Operations, or other go-to-market teams in analytically rigorous environments. Experience deploying proactive signals, recommendations, or decision-support systems into production environments. Experience building and maintaining production data pipelines or ETL/ELT systems. Familiarity with advertising technology, digital media, measurement, or platform-based commercial models. Experience working in high-growth, data-intensive organizations where analytics support multiple stakeholders and workflows. Experience with Salesforce, planning tools, product telemetry, and enterprise data platforms such as Snowflake or Databricks. The Trade Desk does not accept unsolicited resumes from search firm recruiters. Fees will not be paid in the event a candidate submitted by a recruiter without an agreement in place is hired
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.