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

- Design large-scale Invalid Traffic (IVT) and ad fraud detection systems — click spam, install hijacking, SDK spoofing, device farms — using rules-based and ML-driven detection across billions of daily events. - Develop high-performance backend services and data processors (Java, Go, Rust, REST APIs, JDBC) supporting DSP bidding logic, campaign management tools, and reporting APIs. - Operate and scale data infrastructure in PubMatic's in-house data centers — Kafka, Spark, and Hadoop clusters — including capacity planning, performance tuning, storage and compute efficiency, and cost optimization on bare-metal hardware.

We'd Love for You to Have: - 1–5+ years of professional experience in Java backend development and data engineering. - Strong computer science fundamentals including data structures, algorithms, distributed systems, and software design principles.

- 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 data pipelines using Spark, Kafka, Hadoop, and SQL on on-premise / in-house data center infrastructure.

- Strong experience building distributed real-time and batch data pipelines using Spark, Kafka, Hadoop, and SQL on on-premise / in-house data center infrastructure. - Experience designing and optimizing large-scale analytical data platforms for Mobile AdTech using Java, Go, Rust, Spark, Kafka, and SQL. Strong understanding of real-time event processing, distributed data systems, and data warehouse architectures. Familiarity with distributed query engines, modern data lake technologies, and high-performance analytical databases (e.g., Trino, Presto, ClickHouse, Apache Iceberg, Delta Lake, or Apache Hudi) is an added advantage. - Experience processing high-volume streaming data using Spark Streaming, Kafka Streams, or similar technologies.

- Experience processing high-volume streaming data using Spark Streaming, Kafka Streams, or similar technologies. - Programming proficiency in Go and/or Rust for building high-throughput, low-latency data services, event collectors, and stream processors, in addition to Java. - Solid understanding of real-time vs. batch processing trade-offs (Lambda/Kappa architectures), event-time processing, partitioning strategies, schema evolution (Avro/Protobuf/Parquet), and data correctness at scale.

PubMatic is seeking for a passionate Data Engineer to join our Mobile Advertising Platform engineering team. In this role, you will build highly scalable data platforms powering Mobile Programmatic Advertising, RTB bidding systems, attribution, campaign optimization, fraud detection, and real-time analytics. You will design and implement distributed systems capable of processing billions of mobile advertising events every day including ad requests, impressions, clicks, installs, conversions, in-app events, and auction logs. You'll build both real-time event processing and large-scale batch processing systems on PubMatic's in-house data centers, leveraging modern Big Data technologies (Spark, Kafka, Hadoop), high-performance services in Go and Rust, and Generative AI to deliver intelligent analytics and automation solutions. You'll collaborate with Product Managers, ML Engineers, Data Scientists, and Backend Engineers to build next-generation Mobile AdTech products.

- Help shape the future of real-time analytics, attribution, campaign pacing, fraud/IVT detection, and GenAI-powered automation for mobile app monetization. - Build modern data platforms with Spark, Kafka, Hadoop, Go, and Rust on PubMatic's in-house data centers at internet scale, with low latency and high availability. - Collaborate with industry-leading engineers, product managers, data scientists, ML engineers, DSP partners, and publisher teams.

- Hands-on experience developing scalable backend services using Java, REST APIs, JDBC, and relational databases. - Strong experience building distributed real-time and batch data pipelines using Spark, Kafka, Hadoop, and SQL on on-premise / in-house data center infrastructure. - Experience designing and optimizing large-scale analytical data platforms for Mobile AdTech using Java, Go, Rust, Spark, Kafka, and SQL. Strong understanding of real-time event processing, distributed data systems, and data warehouse architectures. Familiarity with distributed query engines, modern data lake technologies, and high-performance analytical databases (e.g., Trino, Presto, ClickHouse, Apache Iceberg, Delta Lake, or Apache Hudi) is an added advantage.

- Experience building scalable, production-grade AI or data platforms. - Strong SQL optimization and query performance tuning skills. - Experience running Big Data platforms on-premise — managing Kafka, Spark, and Hadoop/HDFS clusters, capacity planning, and hardware-aware performance tuning in a private data center environment.

- Experience with CI/CD pipelines, Docker, Kubernetes, and containerized application deployment on bare-metal / in-house data center infrastructure. - Experience with monitoring and observability tools such as Grafana, Prometheus, Datadog, or similar. - Strong debugging and troubleshooting skills in distributed environments.

- Experience running Big Data platforms on-premise — managing Kafka, Spark, and Hadoop/HDFS clusters, capacity planning, and hardware-aware performance tuning in a private data center environment. - Experience with CI/CD pipelines, Docker, Kubernetes, and containerized application deployment on bare-metal / in-house data center infrastructure. - Experience with monitoring and observability tools such as Grafana, Prometheus, Datadog, or similar.

- Design and build highly scalable data platforms capable of processing billions of mobile advertising events with low latency and high availability. - Design and build ultra-low-latency, high-throughput data pipelines processing the full mobile event funnel — ad request → impression → click → install → in-app conversion event — including OpenRTB bid request/response logging, auction analytics, and win-rate/bid-landscape reporting across multiple SSPs and ad exchanges, using Kafka, Spark Streaming, and Hadoop on our in-house data centers. - Architect real-time campaign pacing and budget delivery systems, applying bid optimization techniques so spend controls and bid strategy adjustments react to live auction signals within seconds.

- Design and optimize scalable ETL/ELT pipelines and data models supporting Mobile AdTech use cases such as campaign reporting, attribution, audience segmentation, fraud detection, and revenue analytics. - Develop and tune large-scale batch processing jobs on Spark and Hadoop (HDFS/YARN) — hourly and daily aggregations, backfills, reprocessing, and data reconciliation between real-time and batch layers — with strong workflow orchestration, SLAs, and data-quality checks. - Design large-scale Invalid Traffic (IVT) and ad fraud detection systems — click spam, install hijacking, SDK spoofing, device farms — using rules-based and ML-driven detection across billions of daily events.

- Develop high-performance backend services and data processors (Java, Go, Rust, REST APIs, JDBC) supporting DSP bidding logic, campaign management tools, and reporting APIs. - Operate and scale data infrastructure in PubMatic's in-house data centers — Kafka, Spark, and Hadoop clusters — including capacity planning, performance tuning, storage and compute efficiency, and cost optimization on bare-metal hardware. - Design and develop GenAI-powered agents for analytics, operations, and data enrichment using frameworks such as LangChain, LlamaIndex, LangGraph, or custom orchestration frameworks.

- Strong SQL optimization and query performance tuning skills. - Experience running Big Data platforms on-premise — managing Kafka, Spark, and Hadoop/HDFS clusters, capacity planning, and hardware-aware performance tuning in a private data center environment. - Experience with CI/CD pipelines, Docker, Kubernetes, and containerized application deployment on bare-metal / in-house data center infrastructure.

- Architect and implement real-time streaming pipelines to ingest, process, and analyze billions of advertising events including impressions, clicks, installs, conversions, and in-app events. - Build real-time event processing systems with event-time semantics, windowed aggregations, watermarking, late/out-of-order event handling, deduplication, and exactly-once or idempotent delivery guarantees across Kafka and Spark Structured Streaming. - Design and optimize scalable ETL/ELT pipelines and data models supporting Mobile AdTech use cases such as campaign reporting, attribution, audience segmentation, fraud detection, and revenue analytics.

- Experience designing and optimizing large-scale analytical data platforms for Mobile AdTech using Java, Go, Rust, Spark, Kafka, and SQL. Strong understanding of real-time event processing, distributed data systems, and data warehouse architectures. Familiarity with distributed query engines, modern data lake technologies, and high-performance analytical databases (e.g., Trino, Presto, ClickHouse, Apache Iceberg, Delta Lake, or Apache Hudi) is an added advantage. - Experience processing high-volume streaming data using Spark Streaming, Kafka Streams, or similar technologies. - Programming proficiency in Go and/or Rust for building high-throughput, low-latency data services, event collectors, and stream processors, in addition to Java.