MoEngage is an insights-led customer engagement platform trusted by 1,350+ global consumer brands, including McAfee, Flipkart, Domino’s, Nestle, Deutsche Telekom, and OYO. MoEngage combines data from multiple sources to help brands gain a 360-degree view of their customers.  

MoEngage Analytics arms marketers and product owners with insights into customer behavior. Brands can leverage MoEngage Personalize to orchestrate journeys and build 1:1 conversations across the website, mobile, email, social, and messaging channels. MoEngage Inform, the transactional messaging infrastructure, helps unify promotional and transactional communication to a single platform for better insights and lower costs. MoEngage’s AI Suite helps marketers develop winning copies and creatives, optimize campaigns and channels that boost engagement, and help with faster execution.

For over a decade, consumer brands in 60+ countries have been using MoEngage to power digital experiences for over a billion monthly customers. With offices in 15 countries, MoEngage is backed by Goldman Sachs Asset Management, B Capital, Steadview Capital, Multiples Private Equity, Eight Roads, F-Prime Capital, Matrix Partners, Ventureast, and Helion Ventures.

MoEngage was named a Contender in The Forrester Wave™: Real-Time Interaction Management, Q1 2024 report, and Strong Performer in The Forrester Wave™ 2023 report. MoEngage was also featured as a Leader in the IDC MarketScape: Worldwide Omni-Channel Marketing Platforms for B2C Enterprises 2023.

About the Data Engineering Team at Monengage:
Join our innovative Data Engineering team, where your expertise will play a critical role in architecting and executing the data infrastructure that powers real-time data ingestion, large-scale data lake, and Kafka clusters where more than a million messages per second are produced. Our team is responsible for handling high-volume user and event data, business critical pipelines, not only ensuring that they are robust, high-performing but also scalable and efficient to meet the demands of our dynamic data environment.

• Experience: A minimum of 3-4 years in the data engineering field, demonstrating a track record of managing data infrastructure and pipelines on high scale distributed systems.
• Programming Skills: Expertise in at least one high-level programming language, with a strong preference for candidates proficient in Java and Python.
• Cloud Infrastructure: Proven experience in setting up, maintaining, and optimizing data infrastructures in cloud environments, particularly on AWS or Azure.
• Tech Stack Proficiency: Hands-on experience with a variety of data technologies including, but not limited to:
  • Kafka for stream-processing
  • Kubernetes for container orchestration
  • AWS S3, Athena, and Glue for storage and ETL services
  • Spark for large-scale data processing
  • Debezium for change data capture
  • Apache Airflow for workflow management
  • Any one Streaming frameworks  (Kstream/Flink/Samza/Spark streaming)

• Data Processing: Demonstrable skills in cleansing and standardizing data from diverse sources such as Kafka streams and databases.
• Query Optimization: Proficient in optimizing queries to achieve optimal performance with large datasets, minimizing processing times without sacrificing quality.
• Problem-Solving Abilities: An analytical mindset with robust problem-solving skills, essential for identifying and addressing issues during data integration and schema evolution.
• Cost Optimization Expertise: A keen eye for cost-saving opportunities without compromising on system efficiency. Capable of architecting solutions that are not only robust and scalable but also cost-effective, ensuring optimal resource utilization and avoiding unnecessary expenses in the cloud and data processing environments.