Data Engineer-Mobile Monetization - Big Data, GenAI

About the Role

We are looking 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 leverage modern Big Data technologies, cloud platforms, and Generative AI to build 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.

• 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, Hadoop, and Snowflake on AWS.
• 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.
• 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.
• 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.
• 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 backend services (Java, REST APIs, JDBC) supporting DSP bidding logic, campaign management tools, and reporting APIs.
• Design and develop GenAI-powered agents for analytics, operations, and data enrichment using frameworks such as Lang Chain, Llama Index, Lang Graph, or custom orchestration frameworks.
• Design systems that are highly available, fault tolerant, and optimized for low latency and high throughput.
• Collaborate closely with cross-functional teams to improve platform scalability, reliability, performance, and customer experience.
• Work with Product Managers to define and implement new analytics capabilities and platform features.
• Participate in Agile/Scrum ceremonies including sprint planning, backlog grooming, estimation, retrospectives, and release planning.
• Perform architecture discussions, design reviews, and peer code reviews while promoting software engineering best practices.

• 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.
• Hands-on experience developing scalable backend services using Java, REST APIs, JDBC, and relational databases.
• Strong experience building distributed data pipelines using Spark, Kafka, Hadoop, Snowflake, SQL, and AWS.
• Experience designing and optimizing large-scale analytical data platforms for Mobile AdTech using Java, Spark, Kafka, Snowflake, SQL, and AWS. 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, Click House, 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.
• Good understanding of data warehousing, dimensional modeling, ETL/ELT design, and distributed computing concepts.

• Direct experience with Real-Time Bidding (RTB), OpenRTB protocol, and Mobile DSP/SSP/Ad Exchange architecture.
• Familiarity with mobile SDK event tracking (impressions, clicks, installs, in-app events) and MMP integrations.
• Understanding of attribution methodologies (click-through, view-through, multi-touch based attribution).
• Knowledge of mobile identity solutions (IDFA, GAID, IDFV) and the privacy landscape driving their deprecation.
• Experience with campaign pacing, bid optimization, and budget delivery algorithms.

Background in ad fraud/IVT