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Key Responsibilities and Required Skills for Ad Tech Engineer

💰 $ - $

🎯 Role Definition

An Ad Tech Engineer designs, builds, and optimizes the ad infrastructure that powers programmatic advertising, ad delivery, measurement and monetization. This role combines low-latency distributed systems engineering, ad protocol expertise (RTB/OpenRTB, VAST/VPAID), integration with DSPs/SSPs, analytics and data pipeline development, and close collaboration with product, sales, and ad operations teams. The Ad Tech Engineer owns reliability, performance, privacy compliance (GDPR/CCPA), and instrumentation required to run large-scale ad platforms and SDKs.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Software Engineer (backend) with interest in advertising systems
  • Ad Ops / Implementation Engineer transitioning to engineering
  • Data Engineer or Systems Engineer with experience in streaming and low-latency systems

Advancement To:

  • Senior Ad Tech Engineer / Principal Engineer
  • Technical Lead / Engineering Manager for Monetization or Ads Platform
  • Ad Tech Architect or Head of Ad Infrastructure
  • Product Manager for Ad Products (exchange, SSP, header bidding)

Lateral Moves:

  • Data Engineer / ML Engineer on advertising analytics
  • Ad Operations Manager or Programmatic Strategy lead
  • Sales Engineer or Solutions Architect supporting advertisers/publishers

Core Responsibilities

Primary Functions

  • Design, implement, and maintain high-throughput, low-latency ad serving services (ad server, bid server, cache layers) capable of handling millions of requests per second while meeting strict tail-latency SLAs.
  • Build and extend real-time bidding (RTB/OpenRTB) integrations with DSPs/SSPs, including bid request/response processing, auction logic, and fraud mitigation hooks.
  • Lead the development and optimization of header bidding integrations (Prebid.js, Prebid Server), client-side wrappers and server-side bidding endpoints to increase yield and reduce latency.
  • Implement and maintain ad SDKs (mobile web, iOS, Android, CTV) and server-side ad stitching (SSAI) components, ensuring consistent creatives rendering, tracking, and reporting.
  • Architect and operate streaming data pipelines (Kafka, Kinesis, Pub/Sub) to ingest event logs, impression and click telemetry, and feed real-time analytics and attribution engines.
  • Author and optimize SQL / analytical queries and ETL workflows for campaign reporting, revenue reconciliation, and data-driven product decisions.
  • Design and instrument comprehensive observability: distributed tracing, real-user monitoring (RUM), metrics (Prometheus/Grafana), and centralized logging for ad request lifecycle and auction performance.
  • Implement creative rendering workflows and ad tag management (VAST/VPAID, VMAP, HTML5), ensuring fallback behavior, creative security, and consistent tracking across devices.
  • Collaborate with product and sales teams to specify integrations required by advertising partners, publishers, and demand partners; translate business requirements into technical designs.
  • Optimize network, serialization, and I/O layers to minimize request/response timeouts and reduce bid latency, including use of efficient binary formats, connection pooling, and CDN strategies.
  • Build and maintain caching strategies, key-value stores (Redis/Memcached) and pre-aggregation layers to reduce load on core auction and reporting services.
  • Implement robust attribution, viewability and measurement pipelines; integrate with third-party measurement providers and configure industry-standard metrics.
  • Lead end-to-end campaign setup, trafficking automation, and API interfaces for buyer-facing and publisher-facing tools to streamline operations and reduce manual errors.
  • Design and enforce security, privacy, and compliance controls for PII and user identifiers: consent management, GDPR/CCPA controls, ID deprecation strategies, and secure data handling.
  • Develop ad fraud detection and mitigation systems (anomaly detection, device spoofing checks, invalid traffic filters) and integrate third-party anti-fraud solutions when needed.
  • Implement A/B testing frameworks and experimentation for auction logic, floor price strategies, and feature rollouts to quantify revenue and performance impact.
  • Manage CI/CD, automated testing, canary deployments and blue/green releases for ad platform services to minimize downtime and ensure safe releases.
  • Tune database schemas, indexes and data stores (Postgres, ClickHouse, Druid) for high-cardinality ad event storage, fast aggregations and analytical queries.
  • Lead technical onboarding and documentation for ad partners (demand and supply), including API guides, sample code, and compatibility matrices.
  • Troubleshoot production incidents related to ad delivery, revenue discrepancies, SDK crashes, or partner integrations; lead postmortems and implement long-term fixes.
  • Collaborate with ML engineers to productionize models for bid optimization, predictive pricing, and audience targeting; ensure models meet latency and throughput constraints.
  • Drive monetization experiments and implement pricing floor and yield optimization algorithms using server-side logic and data science inputs.
  • Maintain relationships with key vendor platforms (Google Ad Manager, The Trade Desk, Magnite) and keep integrations up to date with protocol or API changes.
  • Implement media and creative verification workflows to ensure ads comply with policy, brand safety and technical delivery standards.
  • Develop exportable and reusable tooling (traffic validators, simulation harnesses, local RTB emulators) to accelerate partner integrations and QA.
  • Evaluate and onboard cloud-native infrastructure and managed services (AWS/GCP/Azure) for cost-effective scaling, resilience and disaster recovery of ad systems.
  • Drive performance reviews and mentoring within the ad platform engineering team to raise technical quality and domain expertise.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Provide technical guidance to sales and customer success during RFPs and partner onboarding.
  • Create and maintain runbooks and SLA documentation for ad delivery services.
  • Perform capacity planning and cost optimization for cloud resources used by ad stacks.
  • Engage with external standards (IAB, OpenRTB) and contribute feedback or implement new specs.
  • Assist legal/compliance teams with technical details during privacy audits and vendor reviews.

Required Skills & Competencies

Hard Skills (Technical)

  • Strong systems programming and backend engineering experience in one or more languages: Go, Java, C++, or Rust; practical experience with concurrency, memory management and network I/O.
  • Deep knowledge of programmatic advertising protocols and standards: RTB/OpenRTB, VAST, VPAID, VMAP, OpenAuction.
  • Experience integrating with DSPs, SSPs, ad exchanges and header bidding frameworks (Prebid.js / Prebid Server).
  • Expertise in low-latency, high-throughput architectures: request routing, connection multiplexing, and queue management.
  • Proficiency building and operating streaming platforms and event pipelines: Kafka, AWS Kinesis, or Google Pub/Sub.
  • Strong SQL and analytical skills; experience with analytical stores like ClickHouse, Druid, BigQuery, or Redshift.
  • Familiarity with ad measurement, attribution, viewability measurement, and fraud detection concepts and tools.
  • Practical experience with caching, key-value stores and in-memory databases: Redis, Memcached.
  • Experience with cloud platforms and services (AWS, GCP, Azure), containerization (Docker) and orchestration (Kubernetes).
  • Proficient with monitoring and observability stacks: Prometheus, Grafana, ELK/EFK, Jaeger/Zipkin.
  • Experience building SDKs for mobile and CTV environments; knowledge of Web, iOS, Android ad integration nuances and sandboxing.
  • Familiarity with privacy and compliance implementation: consent signals, GDPR/CCPA handling, IDFA/GAID mitigation strategies.
  • Competence with CI/CD, automated testing frameworks, and infrastructure-as-code (Terraform, CloudFormation).
  • Understanding of HTTP/2, gRPC, WebSockets, and best practices for API design and versioning.
  • Experience with message serialization formats (JSON, Protobuf, Avro) and schema management.
  • Proven ability to instrument telemetry and business metrics for revenue, fill rate, CPM, and latency.
  • Familiarity with ML model deployment patterns and feature pipelines for real-time bidding optimization.
  • Knowledge of commercial ad platforms and ad servers: Google Ad Manager, AdButler, SpotX, or similar.
  • Experience with security best practices and cryptography basics for data in transit and at rest.

Soft Skills

  • Strong verbal and written communication; able to translate technical details for product, sales, and ops stakeholders.
  • Problem-solving mindset: diagnose root causes in complex distributed systems and propose pragmatic solutions.
  • Stakeholder management: prioritize partner requirements and balance product, engineering and revenue tradeoffs.
  • Collaboration and mentoring: work cross-functionally with data science, product, operations and business teams.
  • Ownership and accountability: lead projects end-to-end and own reliability and performance outcomes.
  • Adaptability: work effectively in a fast-changing ad tech environment, keeping pace with industry protocol changes.
  • Analytical thinking and data-driven decision making; comfortable using metrics to guide engineering trade-offs.
  • Time management and prioritization in a matrixed organization with competing deadlines.
  • Customer-oriented: empathetic to publisher and buyer needs to create scalable, supportable integrations.
  • Detail-oriented and quality-focused with a bias for automated testing and reproducible deployments.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Software Engineering, Electrical Engineering, or related technical field (or equivalent practical experience).

Preferred Education:

  • Master's degree in Computer Science, Data Science, or an advanced degree with focus on distributed systems, networking, or machine learning.

Relevant Fields of Study:

  • Computer Science / Software Engineering
  • Data Engineering / Data Science
  • Electrical Engineering / Systems Engineering
  • Telecommunications / Network Engineering

Experience Requirements

Typical Experience Range: 3–7 years in backend systems, ideally with 2+ years focused on advertising technologies or programmatic platforms.

Preferred:

  • 5+ years building distributed, low-latency services or ad infrastructure.
  • Direct experience in programmatic ad systems (RTB/OpenRTB, header bidding, ad servers).
  • Proven track record of shipping production systems, incident ownership, and performance tuning for revenue-impacting services.
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