Data Engineer – Mobile Monetization – Big Data, GenAI & Mobile AdTech

PubMatic•Redwood City, CA

4h•$200,000 - $230,000•Hybrid

About The Position

PubMatic is seeking a Data Engineer to join its Mobile Advertising Platform engineering team. The role involves building highly scalable data platforms for mobile programmatic advertising, including real-time bidding, attribution, campaign optimization, fraud detection, and real-time analytics. The systems process billions of mobile advertising events daily, using real-time and batch processing on PubMatic’s in-house data centers and technologies including Spark, Kafka, Hadoop, Go, Rust, and Generative AI. The Data Engineer will work with Product Managers, ML Engineers, Data Scientists, and Backend Engineers to develop mobile AdTech products. The role includes architecting low-latency, high-throughput pipelines and services; operating and optimizing data infrastructure; developing analytics and automation capabilities; and contributing to design, code review, and Agile delivery processes. The position follows PubMatic’s hybrid work schedule: three days in the office and two days working remotely. The stated total compensation range is $200,000–$230,000 USD, with a bonus and benefits package also offered.

Requirements

1–5+ years of professional experience in Java backend development and data engineering.
Bachelor’s degree in engineering or an equivalent degree from a well-known institute or university.
Strong computer science fundamentals, including data structures, algorithms, distributed systems, and software design principles.
Hands-on experience developing scalable backend services using Java, REST APIs, JDBC, and relational databases.
Strong experience building distributed real-time and batch pipelines using Spark, Kafka, Hadoop, and SQL on on-premise or in-house data-center infrastructure.
Experience designing and optimizing large-scale analytical data platforms using Java, Go, Rust, Spark, Kafka, and SQL; understanding of real-time event processing, distributed data systems, and data warehouse architectures.
Experience processing high-volume streaming data using Spark Streaming, Kafka Streams, or similar technologies.
Programming proficiency in Go and/or Rust in addition to Java.
Understanding of real-time versus batch processing trade-offs, Lambda/Kappa architectures, event-time processing, partitioning, schema evolution, and data correctness at scale.
Understanding of data warehousing, dimensional modeling, ETL/ELT design, and distributed computing.
Excellent verbal and written communication skills.
Strong interpersonal skills and ability to collaborate across engineering, product, and customer-facing teams.

Nice To Haves

Familiarity with distributed query engines, modern data lakes, and analytical databases such as Trino, Presto, ClickHouse, Apache Iceberg, Delta Lake, or Apache Hudi.
Mobile AdTech or digital advertising experience, including RTB, OpenRTB, and Mobile DSP/SSP/ad exchange architecture.
Familiarity with mobile SDK event tracking and Mobile Measurement Partner integrations.
Understanding of click-through, view-through, and multi-touch attribution methodologies.
Knowledge of mobile identity solutions such as IDFA, GAID, and IDFV and the privacy landscape affecting them.
Experience with campaign pacing, bid optimization, and budget delivery algorithms.
Background in ad-fraud or IVT detection, including click spam, install fraud, and SDK spoofing.
Production-grade GenAI experience, including LLM integration, orchestration frameworks such as LangChain, LlamaIndex, or LangGraph, prompt engineering, AI evaluation and observability, agents, and workflow automation.
Experience building scalable, production-grade AI or data platforms.
Strong SQL optimization and query performance tuning skills.
Experience managing on-premise Kafka, Spark, and Hadoop/HDFS clusters, capacity planning, and hardware-aware performance tuning.
Experience with CI/CD, Docker, Kubernetes, and containerized application deployment on bare-metal or in-house data-center infrastructure.
Experience with monitoring and observability tools such as Grafana, Prometheus, or Datadog.
Strong distributed-environment debugging and troubleshooting skills.
Ability to achieve stretch goals in a fast-paced, collaborative engineering environment and learn new technologies quickly and independently.
Experience leveraging Generative AI tools and IDEs, such as GitHub Copilot, ChatGPT, Claude, Cursor, or Windsurf, to accelerate development and improve code and data quality, automation, documentation, and debugging.

Responsibilities

Design and build scalable, low-latency, highly available data platforms for processing billions of mobile advertising events.
Build pipelines for the mobile event funnel, OpenRTB logging, auction analytics, and reporting across SSPs and ad exchanges using Kafka, Spark Streaming, and Hadoop.
Architect real-time campaign pacing and budget delivery systems that respond to live auction signals.
Implement streaming pipelines and event-processing systems with event-time semantics, windowing, watermarking, late-event handling, deduplication, and exactly-once or idempotent delivery.
Design and optimize ETL/ELT pipelines and data models for campaign reporting, attribution, audience segmentation, fraud detection, and revenue analytics.
Develop and tune Spark and Hadoop batch jobs, including aggregations, backfills, reprocessing, and reconciliation between real-time and batch layers; provide orchestration, SLAs, and data-quality checks.
Design Invalid Traffic and ad-fraud detection systems using rules-based and ML-driven approaches.
Develop backend services and data processors supporting DSP bidding logic, campaign management tools, and reporting APIs.
Operate and scale Kafka, Spark, and Hadoop clusters in PubMatic’s in-house data centers, including capacity planning, performance tuning, and storage, compute, and cost optimization.
Design and develop GenAI-powered agents for analytics, operations, and data enrichment.
Design systems for high availability, fault tolerance, low latency, and high throughput.
Collaborate with cross-functional teams to improve platform scalability, reliability, performance, and customer experience.
Work with Product Managers to define and implement analytics capabilities and platform features.
Participate in Agile/Scrum ceremonies, including planning, backlog grooming, estimation, retrospectives, and release planning.
Participate in architecture discussions, design reviews, and peer code reviews, and promote software engineering best practices.