24 days ago
Pune, IndiaEntry Level / Mid Level
Responsibilities
- Design and build highly scalable, low-latency data platforms and streaming pipelines processing billions of mobile advertising events.
- Develop pipelines and analytics for RTB auctions, campaign pacing, bid optimization, attribution, audience segmentation, fraud detection, and revenue reporting.
- Build backend Java services, REST APIs, and reporting systems supporting DSP bidding logic and campaign management.
- Design large-scale invalid traffic and ad fraud detection systems using rules-based and machine-learning-driven methods.
- Develop GenAI-powered agents and workflow automation for analytics, operations, and data enrichment.
- Ensure systems are highly available, fault tolerant, scalable, and optimized for throughput and latency.
- Collaborate with product managers, ML engineers, data scientists, backend engineers, and customer-facing teams.
- Participate in architecture discussions, design reviews, code reviews, Agile/Scrum ceremonies, and software engineering best practices.
Requirements
- Bachelor’s degree in engineering or an equivalent degree.
- 1–5+ years of professional experience in Java backend development and data engineering.
- Strong knowledge of data structures, algorithms, distributed systems, software design, data warehousing, dimensional modeling, and ETL/ELT design.
- Hands-on experience with Java, REST APIs, JDBC, relational databases, SQL, Spark, Kafka, Hadoop, Snowflake, and AWS.
- Experience building high-volume streaming systems with Spark Streaming, Kafka Streams, or similar technologies.
- Experience designing analytical data platforms and optimizing SQL and query performance.
- Experience with Mobile AdTech, RTB, OpenRTB, DSP/SSP/ad exchange architecture, mobile SDK events, MMP integrations, attribution, mobile identity, campaign pacing, bid optimization, or ad fraud detection is preferred.
- Experience building production-grade GenAI applications involving LLM integration, prompt engineering, evaluation, observability, agents, and workflow automation.
- Familiarity with Trino, Presto, ClickHouse, Apache Iceberg, Delta Lake, or Apache Hudi is advantageous.
- Experience with CI/CD, Docker, Kubernetes, cloud-native applications, and monitoring tools such as Grafana, Prometheus, Datadog, or AWS is preferred.
- Strong debugging, troubleshooting, communication, collaboration, and independent learning skills.
Benefits
- Hybrid work schedule with three days in the office and two days working remotely.
- Paternity and maternity leave.
- Healthcare insurance.
- Broadband reimbursement.
- Kitchen stocked with healthy snacks and drinks and catered lunches when working in the office.
Tech Stack
Apache HadoopApache KafkaApache SparkAWSClickHouseDatadogDockerGrafanaJavaKubernetesPrestoPrometheusSnowflakeSQL
Categories
BackendData Engineering
